Deep Learning Face Representation by Joint Identification Verification(联合独立验证深度学习人脸)
自然场景图片中的文本检测和定位
[10] He Kaiming, Zhang Xiangyu, Ren Shaoqing, etal. Deep residua 1 learning for image recognition[C] // 2016 IEEE Conf erence on Computer Vision and Pattern Recognit ion (CVPR), 2016: 770-778.
近年来，深度学习领域出现新的卷积神经网络模型，推动了文 本检测领域的发展。场景图片中的文本检测方法大致上可以划分基 于候选区域的方法和基于滑动窗口的方法。基于候选区域的方法主 要利用图像的边缘信息和角点信息，或者根据文本区域通常在灰度、 颜色等特征上的相似性，对文本区域的连通域进行合并作为候选 区域。此类方法通过检测场景图片中的连通域来作为文本的候选区 域。常见的基于候选区域的文本检测方法有Matas等人提出的最大 稳定极值区域（MSER）、极值区域的方法和笔画宽度变换（Stroke
[8JOROUGHI H, SHAKERI M, RAY N, et al. Face recognition us ing mult i-moda1 low-rank dictionary learning[C] // Proceedings of the 2017 IEEE Internationa1 Conference on Image Processing. Piscataway: IEEE, 2017: 1081-1086.
关键词：卷积神经网络（CNN ）;文本定位；最大稳定极值区域（MSER）
深度迁移学习深度学习
深度迁移学习一、深度学习1)ImageNet Classification with Deep Convolutional Neural Networks主要思想：该神经网络有6000万个参数和650,000个神经元，由五个卷积层，以及某些卷积层后跟着的max-pooling层，和三个全连接层，还有排在最后的1000-way的softmax层组成。
使用了非饱和的神经元和一个非常高效的GPU关于卷积运算的工具。
1、采用了最新开发的正则化方法，称为“dropout”。
2、采用ReLU来代替传统的tanh引入非线性，；3、采用2块显卡来进行并行计算，减少了更多显卡需要主机传递数据的时间消耗，在结构上，部分分布在不同显卡上面的前后层节点之间无连接，从而提高了训练速度；4、同层相邻节点的响应进行局部归一化提高了识别率（top5错误率降低1.2%）；5、有交叠的pooling（top5错误率降低0.3%）；体系架构：（1）ReLU训练带ReLUs的深度卷积神经网络比带tanh单元的同等网络要快好几倍。
如下图，带ReLU的四层卷积神经网络（实线）在CIFAR-10数据集上达到25%训练误差率要比带tanh神经元的同等网络（虚线）快六倍。
（2）在多个GPU上训练（3）局部响应归一化具体见Very Deep Convolutional Networks for Large-Scale Image Recognition（4）重叠Pooling每个网格间隔距离为s，而每一次进行降采样将从网格中心为中心，采样z*z个像素。
如果s=z，则与传统方法相同，而如果s<z，则会进行重复采样。
本文章将s=2，z=3，成功的将Top-1和Top-5的错误率分别降低了0.4%和0.3%（与s=2，z=2相比）。
而且，在实验中发现，采用重叠采样将会略微更难产生过拟合。
（5）总体结构该网络包括八个带权层；前五层是卷积层，剩下三层是全连接层。
深度学习转变图像识别
深度学习转变图像识别近年来，随着深度学习的快速发展，图像识别技术得到了前所未有的提升。
传统的图像处理技术已经无法满足现代社会对图像识别的高精度、高效率的需求。
深度学习通过模拟人脑神经网络的工作方式，为图像识别提供了全新的视角和方法，极大地推动了这一领域的进步。
深度学习的基本原理深度学习是一种基于人工神经网络的机器学习方法。
它通过构建多层次的神经网络来进行特征提取与模式识别。
其核心思想是通过大量的数据进行自我训练，从而自动提取特征，无需手动设计特征提取算法。
深度学习可以分为以下几个主要组成部分：神经元与神经网络：深度学习中的基本单位是神经元，每个神经元接收输入，进行加权计算，并通过激活函数产生输出。
多个神经元可以通过不同层次组合形成神经网络。
前馈与反向传播：在神经网络中，信息从输入层传递至输出层被称为前馈。
为了优化网络参数，使用反向传播算法根据输出结果调整权重，从而提高模型的准确性。
激活函数：激活函数用于引入非线性因素，使得神经网络能够更好地解决复杂问题。
常见的激活函数包括Sigmoid函数、ReLU （Rectified Linear Unit）和Tanh（双曲正切）等。
损失函数与优化器：损失函数用以衡量模型预测值与真实值之间的差距，而优化器则通过调整权重来最小化损失函数，从而提高模型性能。
图像识别的发展历程在深度学习兴起之前，传统的图像识别方法主要依靠手工设计特征。
这些方法包括但不限于边缘检测、纹理分析和形状匹配等。
然而，这些技术面对复杂场景时力不从心，尤其是处理多样化、复杂性高、变化迅速的自然图像时常常会出现错误。
2012年，尤尔根·许曼（Geoffrey Hinton）团队在ImageNet挑战赛中提出了卷积神经网络（CNN），标志着图像识别领域进入了深度学习时代。
此后，不同类型的深度学习模型相继被提出，包括但不限于以下几种：卷积神经网络（CNN）：最为经典和广泛应用的一种网络架构，特别适合处理二维图像，其优点在于能够自动提取局部特征并保持空间关系。
Deep Learning Face Representation from Predicting 10,000 Classes
Deep Learning Face Representation from Predicting10,000Classes Yi Sun1Xiaogang Wang2Xiaoou Tang1,31Department of Information Engineering,The Chinese University of Hong Kong2Department of Electronic Engineering,The Chinese University of Hong Kong3Shenzhen Institutes of Advanced Technology,Chinese Academy of Sciences sy011@.hk xgwang@.hk xtang@.hkAbstractThis paper proposes to learn a set of high-level feature representations through deep learning,referred to as Deep hidden IDentity features(DeepID),for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks,whilst they can be generalized to other tasks(such as verification)and new identities unseen in the training set.Moreover,the generalization capability of DeepID increases as more face classes are to be predicted at training.DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks(ConvNets).When learned as classifiers to recognize about10,000face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy,these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons.The proposed features are extracted from various face regions to form complementary and over-complete representations.Any state-of-the-art classifiers can be learned based on these high-level representations for face verification.97.45%verification accuracy on LFW is achieved with only weakly aligned faces.1.IntroductionFace verification in unconstrained conditions has been studied extensively in recent years[21,15,7,34,17,26, 18,8,2,9,3,29,6]due to its practical applications and the publishing of LFW[19],an extensively reported dataset for face verification algorithms.The current best-performing face verification algorithms typically represent faces with over-complete low-level features,followed by shallow models[9,29,6].Recently,deep models such as ConvNets[24]have been proved effective for extracting high-level visual features[11,20,14]and are used for face verification[18,5,31,32,36].Huang et al.[18] learned a generative deep model without supervision.Cai Figure1.An illustration of the feature extraction process.Arrows indicate forward propagation directions.The number of neurons in each layer of the multiple deep ConvNets are labeled beside each layer.The DeepID features are taken from the last hidden layer of each ConvNet,and predict a large number of identity classes. Feature numbers continue to reduce along the feature extraction cascade till the DeepID layer.et al.[5]learned deep nonlinear metrics.In[31],the deep models are supervised by the binary face verification target.Differently,in this paper we propose to learn high-level face identity features with deep models through face identification,i.e.classifying a training image into one of n identities(n≈10,000in this work).This high-dimensional prediction task is much more challenging than face verification,however,it leads to good generalization of the learned feature representations.Although learned through identification,these features are shown to be effective for face verification and new faces unseen in the training set.We propose an effective way to learn high-level over-complete features with deep ConvNets.A high-level illustration of our feature extraction process is shown in Figure1.The ConvNets are learned to classify all the faces available for training by their identities,with the last hidden layer neuron activations as features(referred to as2014 IEEE Conference on Computer Vision and Pattern RecognitionDeep hidden IDentity features or DeepID).Each ConvNet takes a face patch as input and extracts local low-level features in the bottom layers.Feature numbers continue to reduce along the feature extraction cascade while gradually more global and high-level features are formed in the top layers.Highly compact160-dimensional DeepID is acquired at the end of the cascade that contain rich identity information and directly predict a much larger number(e.g., 10,000)of identity classes.Classifying all the identities simultaneously instead of training binary classifiers as in [21,2,3]is based on two considerations.First,it is much more difficult to predict a training sample into one of many classes than to perform binary classification.This challenging task can make full use of the super learning capacity of neural networks to extract effective features for face recognition.Second,it implicitly adds a strong regularization to ConvNets,which helps to form shared hidden representations that can classify all the identities well.Therefore,the learned high-level features have good generalization ability and do not over-fit to a small subset of training faces.We constrain DeepID to be significantly fewer than the classes of identities they predict,which is key to learning highly compact and discriminative features. We further concatenate the DeepID extracted from various face regions to form complementary and over-complete rep-resentations.The learned features can be well generalized to new identities in test,which are not seen in training, and can be readily integrated with any state-of-the-art face classifiers(e.g.,Joint Bayesian[8])for face verification.Our method achieves97.45%face verification accuracy on LFW using only weakly aligned faces,which is almost as good as human performance of97.53%.We also observe that as the number of training identities increases,the verification performance steadily gets improved.Although the prediction task at the training stage becomes more challenging,the discrimination and generalization ability of the learned features increases.It leaves the door wide open for future improvement of accuracy with more training data.2.Related workMany face verification methods represent faces by high-dimensional over-complete face descriptors,followed by shallow models.Cao et al.[7]encoded each face image into 26K learning-based(LE)descriptors,and then calculated the L2distance between the LE descriptors after PCA.Chen et al.[9]extracted100K LBP descriptors at dense facial landmarks with multiple scales and used Joint Bayesian[8] for verification after PCA.Simonyan et al.[29]computed 1.7M SIFT descriptors densely in scale and space,encoded the dense SIFT features into Fisher vectors,and learned lin-ear projection for discriminative dimensionality reduction. Huang et al.[17]combined1.2M CMD[33]and SLBP [1]descriptors,and learned sparse Mahalanobis metrics for face verification.Some previous studies have further learned identity-related features based on low-level features.Kumar et al.[21]trained attribute and simile classifiers to detect facial attributes and measure face similarities to a set of reference people.Berg and Belhumeur[2,3]trained classifiers to distinguish the faces from two different people.Features are outputs of the learned classifiers.They used SVM classifiers,which are shallow structures,and their learned features are still relatively low-level.In contrast,we classify all the identities from the training set simultaneously.More-over,we use the last hidden layer activations as features instead of the classifier outputs.In our ConvNets,the neuron number of the last hidden layer is much smaller than that of the output,which forces the last hidden layer to learn shared hidden representations for faces of different people in order to well classify all of them,resulting in highly discriminative and compact features with good generalization ability.A few deep models have been used for face verification or identification.Chopra et al.[10]used a Siamese network [4]for deep metric learning.The Siamese network extracts features separately from two compared inputs with two identical sub-networks,taking the distance between the outputs of the two sub-networks as dissimilarity.[10] used deep ConvNets as the sub-networks.In contrast to the Siamese network in which feature extraction and recognition are jointly learned with the face verification target,we conduct feature extraction and recognition in two steps,with thefirst feature extraction step learned with the target of face identification,which is a much stronger supervision signal than verification.Huang et al.[18] generatively learned features with CDBNs[25],then used ITML[13]and linear SVM for face verification.Cai et al.[5]also learned deep metrics under the Siamese network framework as[10],but used a two-level ISA network[23] as the sub-networks instead.Zhu et al.[35,36]learned deep neural networks to transform faces in arbitrary poses and illumination to frontal faces with normal illumination,and then used the last hidden layer features or the transformed faces for face recognition.Sun et al.[31]used multiple deep ConvNets to learn high-level face similarity features and trained classification RBM[22]for face verification.Their features are jointly extracted from a pair of faces instead of from a single face.3.Learning DeepID for face verification3.1.Deep ConvNetsOur deep ConvNets contain four convolutional layers (with max-pooling)to extract features hierarchically,fol-lowed by the fully-connected DeepID layer and the softmax output layer indicating identity classes.The input is39×Figure 2.ConvNet structure.The length,width,and height of each cuboid denotes the map number and the dimension of each map for all input,convolutional,and max-pooling layers.The inside small cuboids and squares denote the 3D convolution kernel sizes and the 2D pooling region sizes of convolutional and max-pooling layers,respectively.Neuron numbers of the last two fully-connected layers are marked beside each layer.31×k for rectangle patches,and 31×31×k for square patches,where k =3for color patches and k =1for gray patches.Figure 2shows the detailed structure of the ConvNet which takes 39×31×1input and predicts n (e.g .,n =10,000)identity classes.When the input sizes change,the height and width of maps in the following layers will change accordingly.The dimension of the DeepID layer is fixed to 160,while the dimension of the output layer varies according to the number of classes it predicts.Feature numbers continue to reduce along the feature extraction hierarchy until the last hidden layer (the DeepID layer),where highly compact and predictive features are formed,which predict a much larger number of identity classes with only a few features.The convolution operation is expressed asy j (r )=max 0,b j (r )+ik ij (r )∗x i (r ),(1)where x i and y j are the i -th input map and the j -th outputmap,respectively.k ij is the convolution kernel between the i -th input map and the j -th output map.∗denotes convolution.b j is the bias of the j -th output map.We use ReLU nonlinearity (y =max (0,x ))for hidden neurons,which is shown to have better fitting abilities than the sigmoid function [20].Weights in higher convolutional layers of our ConvNets are locally shared to learn different mid-or high-level features in different regions [18].r in Equation 1indicates a local region where weights are shared.In the third convolutional layer,weights are locally shared in every 2×2regions,while weights in the fourth convolutional layer are totally unshared.Max-pooling is formulated asy ij,k =max 0≤m,n<sx i j ·s +m,k ·s +n ,(2)where each neuron in the i -th output map y i pools over an s ×s non-overlapping local region in the i -th input map x i.Figure 3.Top:ten face regions of medium scales.The five regionsin the top left are global regions taken from the weakly aligned faces,the other five in the top right are local regions centered around the five facial landmarks (two eye centers,nose tip,and two mouse corners).Bottom:three scales of two particular patches.The last hidden layer of DeepID is fully connected to both the third and fourth convolutional layers (after max-pooling)such that it sees multi-scale features [28](features in the fourth convolutional layer are more global than those in the third one).This is critical to feature learning because after successive down-sampling along the cascade,the fourth convolutional layer contains too few neurons and becomes the bottleneck for information propagation.Adding the bypassing connections between the third con-volutional layer (referred to as the skipping layer)and the last hidden layer reduces the possible information loss in the fourth convolutional layer.The last hidden layer takes the functiony j =max0,ix 1i ·w 1i,j +ix 2i ·w 2i,j +b j,(3)where x 1,w 1,x 2,w 2denote neurons and weights in thethird and fourth convolutional layers,respectively.It lin-early combines features in the previous two convolutional layers,followed by ReLU non-linearity.The ConvNet output is an n -way softmax predicting the probability distribution over n different identities.y i =exp(y i) n j =1exp(y j ),(4)where yj= 160i =1x i ·w i,j +b j linearly combines the 160DeepID features x i as the input of neuron j ,and y j is its output.The ConvNet is learned by minimizing −log y t ,with the t -th target class.Stochastic gradient descent is used with gradients calculated by back-propagation.3.2.Feature extractionWe detectfive facial landmarks,including the two eye centers,the nose tip,and the two mouth corners,with the facial point detection method proposed by Sun et al.[30]. Faces are globally aligned by similarity transformation according to the two eye centers and the mid-point of the two mouth corners.Features are extracted from60face patches with ten regions,three scales,and RGB or gray channels.Figure3shows the ten face regions and the three scales of two particular face regions.We trained 60ConvNets,each of which extracts two160-dimensional DeepID vectors from a particular patch and its horizontally flipped counterpart.A special case is patches around the two eye centers and the two mouth corners,which are not flipped themselves,but the patches symmetric with them (for example,theflipped counterpart of the patch centered on the left eye is derived byflipping the patch centered on the right eye).The total length of DeepID is19,200 (160×2×60),which is ready for thefinal face verification.3.3.Face verificationWe use the Joint Bayesian[8]technique for face ver-ification based on the DeepID.Joint Bayesian has been highly successful for face verification[9,6].It represents the extracted facial features x(after subtracting the mean) by the sum of two independent Gaussian variablesx=μ+ ,(5) whereμ∼N(0,Sμ)represents the face identity and ∼N(0,S )the intra-personal variations.Joint Bayesian models the joint probability of two faces given the intra-or extra-personal variation hypothesis,P(x1,x2|H I)and P(x1,x2|H E).It is readily shown from Equation5that these two probabilities are also Gaussian with variationsΣI=Sμ+S SμSμSμ+S(6)andΣE=Sμ+S 00Sμ+S,(7)respectively.Sμand S can be learned from data with EM algorithm.In test,it calculates the likelihood ratior(x1,x2)=log P(x1,x2|H I)P(x1,x2|H E),(8)which has closed-form solutions and is efficient.We also train a neural network for verification and com-pare it to Joint Bayesian to see if other models can also learn from the extracted features and how much the features and a good face verification model contribute to the performance, respectively.The neural network contains one inputlayer Figure 4.The structure of the neural network used for face verification.The layer type and dimension are labeled beside each layer.The solid neurons form a subnetwork.taking the DeepID,one locally-connected layer,one fully-connected layer,and a single output neuron indicating face similarities.The input features are divided into60 groups,each of which contains640features extracted from a particular patch pair with a particular ConvNet.Features in the same group are highly correlated.Neurons in the locally-connected layer only connect to a single group of features to learn their local relations and reduce the feature dimension at the same time.The second hidden layer is fully-connected to thefirst hidden layer to learn global relations.The single output neuron is fully connected to the second hidden layer.The hidden neurons are ReLUs and the output neuron is sigmoid.An illustration of the neural network structure is shown in Figure4.It has38,400input neurons with19,200DeepID features from each patch,and 4,800neurons in the following two hidden layers,with every80neurons in thefirst hidden layer locally connected to one of the60groups of input neurons.Dropout learning[16]is used for all the hidden neu-rons.The input neurons cannot be dropped because the learned features are compact and distributed representa-tions(representing a large number of identities with very few neurons)and have to collaborate with each other to represent the identities well.On the other hand,learning high-dimensional features without dropout is difficult due to gradient diffusion.To solve this problem,wefirst train60 subnetworks,each with features of a single group as input.A particular subnetwork is illustrated in Figure4.We then use thefirst-layer weights of the subnetworks to initialize those of the original network,and tune the second and third layers of the original network with thefirst layer weights clipped.4.ExperimentsWe evaluate our algorithm on LFW,which reveals the state-of-the-art of face verification in the wild.Though LFW contains5749people,only85have more than15 images,and4069people have only one image.It is inadequate to train identity classifiers with so few images per person.Instead,we trained our model on CelebFaces[31]and tested on LFW(Section4.1-4.3).CelebFaces contains87,628face images of5436celebrities from the Internet,with approximately16images per person on average.People in LFW and CelebFaces are mutually exclusive.We randomly choose80%(4349)people from Celeb-Faces to learn the DeepID,and use the remaining20% people to learn the face verification model(Joint Bayesian or neural networks).For feature learning,ConvNets are supervised to classify the4349people simultaneouslyfrom a particular kind of face patches and theirflipped counterparts.We randomly select10%images of each training person to generate the validation data.After each training epoch,we observe the top-1validation set error rates and select the model that provides the lowest one.In face verification,our feature dimension is reduced to150by PCA before learning the Joint Bayesian model. Performance almost retains in a wide range of dimensions. In test,each face pair is classified by comparing the Joint Bayesian likelihood ratio to a threshold optimized in the training data.To evaluate the performance of our approach at an even larger training scale in Section4.4,we extend CelebFaces to the CelebFaces+dataset,which contains202,599face images of10,177celebrities.Again,people in LFW and CelebFaces+are mutually exclusive.The ConvNet structure and feature extraction process described in the previous section remains unchanged.4.1.Multi-scale ConvNetsWe verify the effectiveness of directly connecting neu-rons in the third convolutional layer(after max-pooling) to the last hidden layer(the DeepID layer),such that it sees both the third and fourth convolutional layer features, forming the so-called multi-scale ConvNets.It also results in reducing feature numbers from the convolutional layers to the DeepID layer(shown in Figure1),which helps the latter to learn higher-level features in order to well represent the face identities with fewer neurons.Figure5compares the top-1validation set error rates of the60ConvNets learned to classify the4349classes of identities,either with or without the skipping layer.The lower error rates indicate the better hidden features learned.Allowing the DeepID to pool over multi-scale features reduces validation errors by an average of4.72%.It actually also improves thefinal face verification accuracy from95.35%to96.05%when concatenating the DeepID from the60ConvNets and using Joint Bayesian for face verification.4.2.Learning effective featuresClassifying a large number of identities simultaneously is key to learning discriminative and compact hidden features.To verify this,we increase the identity classes Figure5.Top-1validation set error rates of the60ConvNets trained on the60different patches.The blue and red markers show error rates of the conventional ConvNets(without the skipping layer)and the multi-scale ConvNets,respectively.for training exponentially(and output neuron numbers correspondingly)from136to4349whilefixing the neuron numbers in all previous layers(the DeepID is kept to be 160dimensional).We observe the classification ability of ConvNets(measured by the top-1validation set error rates) and the effectiveness of the learned hidden representations for face verification(measured by the test set verification accuracy)with the increasing identity classes.The input is a single patch covering the whole face in this experiment.As shown in Figure6,both Joint Bayesian and neural network improve linearly in verification accuracy when the identity classes double.The improvement is significant.When identity classes increase32times from136to4349,the accuracy increases by10.13%and8.42%for Joint Bayesian and neural networks,respectively,or2.03%and1.68%on average,respectively,whenever the identity classes double. At the same time,the validation set error rates drop,even when the predicted classes are tens of times more than the last hidden layer neurons,as shown in Figure7.This phenomenon indicates that ConvNets can learn from classi-fying each identity and form shared hidden representations that can classify all the identities well.More identity classes help to learn better hidden representations that can distinguish more people(discriminative)without increasing the feature length(compact).The linear increasing of test accuracy with respect to the exponentially increasing training data indicates that our features would be further improved if even more identities are available.Examples of the160-dimensional DeepID learned from the4349training identities and extracted from LFW test pairs are shown in Figure8.Wefind that faces of the same identity tend to have more commonly activated neurons(positive features being in the same position)than those of different identities. So the learned features extract identity information.We also test the4349-dimensional classifier outputs as features for face verification.Joint Bayesian only achieves approximately66%accuracy on these features,while the neural network fails,where it accounts all the face pairs asFigure6.Face verification accuracy of Joint Bayesian(red line) and neural network(blue line)learned from the DeepID,where the ConvNets are trained with136,272,544,1087,2175,and4349 classes,respectively.Figure7.Top-1validation set error rates of ConvNets learned to classify136,272,544,1087,2175,and4349classes,respectively. positive or negative pairs.With so many classes and few samples for each class,the classifier outputs are diverse and unreliable,therefore cannot be used as features.4.3.Over-complete representationWe evaluate how much combining features extracted from various face patches would contribute to the perfor-mance.We train the face verification model with features from k patches(k=1,5,15,30,60).It is impossible to numerate all the possible combinations of patches,so we select the most representative ones.We report the best-performing single patch(k=1),the global color patches in a single scale(k=5),all the global color patches (k=15),all the color patches(k=30),and all the patches (k=60).As shown in Figure9,adding more features from various regions,scales,and color channels consistently improves the bing60patches increases the accuracy by4.53%and5.27%over best single patch for Joint Bayesian and neural networks,respectively.We achieve96.05%and94.32%accuracy using Joint Bayesian and neural networks,respectively.The curves show that the performance may be further improved if more features areextracted.Figure8.Examples of the learned160-dimensional DeepID.The left column shows three test pairs in LFW.Thefirst two pairs are of the same identity,the third one is of different identities.The corresponding features extracted from each patch are shown in the right.The features are in one dimension.We rearrange them as 5×32for the convenience of illustration.The feature values are non-negative since they are taken from the ReLUs.Approximately 40%features have positive values.The brighter squares indicate higher values.Figure9.Test accuracy of Joint Bayesian(red line)and neural networks(blue line)using features extracted from1,5,15,30, and60patches.Performance consistently improves with more features.Joint Bayesian is approximately1.8%better on average than neural networks.4.4.Method comparisonTo show how our algorithm would benefit from more training data,we enlarge the CelebFaces dataset to Celeb-Faces+,which contains202,599face images of10,177 celebrities.People in CelebFaces+and LFW are mutually exclusive.We randomly choose8700people from Celeb-Faces+to learn the DeepID,and use the remaining1477 people to learn Joint Bayesian for face verification.Since extracting DeepID from many different face patches also helps,we increase the patch number to100by usingfive different scales of patches instead of three.This results ina32,000-dimensional DeepID feature vector,which is then reduced to150dimensions by PCA.Joint Bayesian learned on this150-dimensional feature vector achieves97.20% test accuracy on LFW.Due to the difference in data distributions,models well fitted to CelebFaces+may not have equal generalization ability on LFW.To solve this problem,Cao et al.[6] proposed a practical transfer learning algorithm to adapt the Joint Bayesian model from the source domain to the target domain.We implemented their algorithm by using the1477 people from CelebFaces+as the source domain data and nine out of ten folders from LFW as the target domain data for transfer learning Joint Bayesian,and conduct ten-fold cross validation on LFW.The transfer learning Joint Bayesian based on our DeepID features achieves97.45% test accuracy on LFW,which is on par with the human-level performance of97.53%.We compare with the state-of-the-art face verification methods on LFW.In the comparison,we report three results.Thefirst two are trained on CelebFaces and CelebFaces+,respectively,without transfer learning,and tested on LFW.The third one is trained on CelebFaces+ with transfer learning on LFW.Table1comprehensively compares the accuracies,the number of facial points used for alignment,the number of outside training images(if applicable),and thefinal feature dimensions for each face (if applicable).Low feature dimensions indicate efficient face recognition systems.Figure10compares the ROC curves.Our DeepID learning method achieves the best performance on LFW.Thefirst four best methods compared used dense facial landmarks,while our faces are weakly aligned with onlyfive points.The deep learning work (DeepFace)[32]independently developed by Facebook at the same time of this paper achieved the second best performance of97.25%accuracy on LFW.It utilized3D alignment and pose transform as preprocessing,and more than seven million outside training images plus training images from LFW.5.Conclusion and DiscussionThis paper proposed to learn effective high-level features revealing identities for face verification.The features are built on top of the feature extraction hierarchy of deep ConvNets and are summarized from multi-scale mid-level features.By representing a large amount of different identities with a small number of hidden variables,highly compact and discriminative features are acquired.The features extracted from different face regions are comple-mentary and further boost the performance.It achieved 97.45%face verification accuracy on LFW,while only requiring weakly aligned faces.Even more compact and discriminative DeepID can be learned if more identities are available to increasethe Figure10.ROC comparison with the state-of-the-art face verifica-tion methods on LFW.TL in our method means transfer learning Joint Bayesian.dimensionality of prediction at the training stage.We look forward to larger training sets to further boost our performance.A recent work[27]reported98.52%accuracy on LFW with Gaussian Processes and multi-source training sets,achieving even higher than human performance.This could be due to the fact that the nonparametric Bayesian kernel method can adapt model complexity to data distri-bution.Gaussian processes can also be modeled with deep learning[12].This could be another interesting direction to be explored in the future.AcknowledgementWe thank Xiaoxiao Li and Cheng Li for their help and discussion.This work is partially supported by”CUHK Computer Vision Cooperation”grant from Huawei,the General Research Fund sponsored by the Research Grants Council of Hong Kong(Project No.CUHK416510and 416312),National Natural Science Foundation of China (91320101),and Guangdong Innovative Research Team Program(No.201001D010*******).References[1]T.Ahonen and M.Pietikainen.Soft histograms for local binarypatterns.2007.2[2]T.Berg and P.Belhumeur.Tom-vs-Pete classifiers and identity-preserving alignment for face verification.In Proc.BMVC,2012.1,2,8[3]T.Berg and P.Belhumeur.POOF:Part-based one-vs-one featuresforfine-grained categorization,face verification,and attribute esti-mation.In Proc.CVPR,2013.1,2[4]J.Bromley,I.Guyon,Y.Lecun,E.S¨a ckinger,and R.Shah.Signatureverification using a“Siamese”time delay neural network.In Proc.NIPS,1994.2[5]X.Cai,C.Wang,B.Xiao,X.Chen,and J.Zhou.Deep nonlinear met-ric learning with independent subspace analysis for face verification.In ACM Multimedia,2012.1,2。
人脸识别技术与生物特征识别培训ppt
算法优化
硬件升级
持续优化人脸识别算法，提高识别速 度和准确率。
升级硬件设备，提高人脸识别系统的 处理能力和响应速度。
数据训练
使用大规模、多样化的数据集进行训 练，提高人脸识别模型的泛化能力。
05
培训内容与实践
人脸识别技术基础培训
人脸识别技术原理
详细介绍人脸识别技术的原理、算法和实现过程，包括特征提取 、比对和识别等关键技术。
分类
按照所利用的特征类型，生物特征识 别技术可分为基于生理特征和基于行 为特征的识别技术。常见生物特征识来自技术介绍0102
03
04
指纹识别
利用指纹的唯一性和稳定性进 行身份鉴别。
虹膜识别
通过分析眼睛的虹膜纹理进行 身份鉴别。
视网膜识别
通过分析眼睛的视网膜结构进 行身份鉴别。
面部识别
通过分析人的面部特征进行身 份鉴别。
人脸识别技术与生物 特征识别培训
汇报人：可编辑
xx年xx月xx日
• 人脸识别技术概述 • 人脸识别的关键技术 • 生物特征识别技术介绍 • 人脸识别技术的挑战与解决方案 • 培训内容与实践 • 总结与展望
目录
01
人脸识别技术概述
人脸识别技术的定义与原理
总结词 人脸识别技术是一种基于生物特征识 别技术，通过计算机图像处理和人工 智能算法，自动识别和验证个人身份 的技术。
个人隐私。
THANKS
感谢观看
比对
将提取出的特征与数据库中的特征进行比对，以实现人脸的识别或验证。
深度学习在人脸识别中的应用
深度学习模型
深度学习模型如卷积神经网络（CNN）被广泛应用于人脸识 别，能够自动提取高层次的特征表示。
深度学习--人脸识别
卷积波尔兹曼机（Convolutional RBM）
卷积过程：用一个可训练的滤波器fx去卷积一个输入的 图像（第一阶段是输入的图像，后面的阶段就是Feature Map了），然后加一个偏置bx，得到卷积层Cx。 子采样过程：每邻域n个像素通过池化（pooling）步骤 变为一个像素，然后通过标量Wx+1加权，再增加偏置bx+1， 然后通过一个sigmoid激活函数，产生一个大概缩小n倍的 特征映射图Sx+1。
深度信念网络（ Deep Belief Networks ）
深度信念网络是一个包含多层隐层（隐层数大于2） 的概率模型，每一层从前一层的隐含单元捕获高度相关 的关联。
DBNs是一个概率生成模型，与传统的判别模型 的神经网络相对，生成模型是建立一个观察数据和 标签之间的联合分布，对P(Observation|Label)和 P(Label|Observation)都做了评估。 典型的DNBs,可视数据v和隐含向量h的关系可 以用概率表示成如下所示形式：
深度模型(Deep models)
●受限波尔兹曼机RBM ●深度信念网络DBN ●卷积受限波尔兹曼机CRBM ●混合神经网络-受限波尔兹曼机CNN-RBM
…….
“深度模型”是手段， “特征学习”是目的！
深度学习
1.什么是深度学习？ 2.深度学习的基本思想
3.深度学习的常用方法
1）自动编码机(AutoEncoder) 2）稀疏编码（Sparse Coding） 3）受限波尔兹曼机（Restrict Boltzmann Machine , RBM）
(a)LBP：Local Binary Pattern(局部二值模式) (b)LE:an unsupervised feature learning method,PCA (c)CRBM:卷积受限波尔兹曼机 (d)FIP:Face Identity-Preserving
深度学习文献阅读笔记（1）
深度学习⽂献阅读笔记（1） 转眼间已经研⼆了。
突然想把曾经看过的⽂献总结总结与⼤家分享，留作纪念，⽅便以后參考。
1、深度追踪：通过卷积⽹络进⾏差异特征学习的视觉追踪(DeepTrack:Learning Discriminative Feature Representations by Convolutional Neural Networks for visual Tracking）（英⽂，会议论⽂，2014年。
EI检索） 将卷积神经⽹络⽤于⽬标跟踪的⼀篇⽂章，可将CNN不只能够⽤做模式识别。
做⽬标跟踪也是能够。
毕竟本质上是⼀种特征提取的⼿段。
2、基于深度学习的车标识别⽅法研究（中⽂。
期刊，2015年。
知⽹） 将传统CNN⽤于车标识别，先进⾏车标定位提取。
在送⼊CNN中进⾏训练，最后採⽤⽀持向量机进⾏分类，属于⽼⽅法新问题。
实验硬件配置：主频2.80GHZCPU，2G内存，未⽤到GPU加速。
3、基于深度学习⽹络的射线图像缺陷识别⽅法（中⽂，期刊，2014年，知⽹） 将CNN直接⽤于射线图像缺陷检測，⽼⽅法新问题。
对CNN结构描写叙述得⾮常清楚，适合CNN⼊门。
4、深度学习及其在⽬标和⾏为识别中的新进展（中⽂，期刊，2014年，知⽹） 主要综述了深度学习中⾃编码器和限制玻尔兹曼机的结构以及应⽤进展。
综述⽐較全⾯，也够权威。
对两者的原理和改进进展都描写叙述得⾮常清楚，指出“深度学习得到的是⼀个多层深度结构，信号在这个多层结构中进⾏传播，最后得到信号的表达。
学习到多层的⾮线性的函数关系，更好的对视觉信息进⾏建模”，值得參考。
5、基于超像素卷积神经⽹络的显著性⽬标检測（Super CNN:A Superpixel wise Convolutional Neural Network for salient object detection）（英⽂，期刊，2015年，IEEE检索） CNN在⽬标检測领域的应⽤，先对图像进⾏超像素切割，得到三个序列（超像素序列，⼀个空间核矩阵。
基于深度学习的人脸鉴伪与识别技术研究与原型实现
2.2.1 VGG.................................................................................................... 13
和相关技术 ..................................................................................... 8
第二章 基础理
2.1 卷积神经网络 ................................................................................................ 8
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结合CNN不同层信息的全变量建模人脸特征表达学习方法
第33卷第8期 2017年8月信号处理JOURNAL OF SIGNAL PROCESSINGVol. 33 No. 8Aug. 2017文章编号：1003-0530(2017)08-1073-09结合CNN不同层信息的全变量建模人脸特征表达学习方法洪新海宋彦(中国科学技术大学语音及语言信息处理国家工程实验室，安徽合肥230027)摘要：如何学习有效的人脸特征表达是人脸识别的关键性问题。
现有基于卷积神经网络（ConWut腿al Neuml Networks,CNN)的人脸深度特征表达学习方法大多在人脸图像经过了有效检测和校正的情况下，能够获得优异的性 會g,而在复杂场景下其推广性和鲁棒性受到极大限制。
对此，本文提出了结合CNN不同层信息的全变量建模人脸 特征表达学习方法，将提取的人脸局部深度特征中所包含的差异信息按照子空间进行建模，有效聚合局部深度特 征的同时得到人脸在低维子空间的特征表达(iVector)。
在IJB-A(IARPA Janus Benchmark A)上的实验结果表明，与 现有的深度特征表达相比，该方法学习得到的人脸iVector表达能够显著提升人脸识别系统的识别性能和计算效率。
关键词：卷积神经网络；全变量建模；人脸识别；人脸特征表达中图分类号：TP391.4 文献标识码：A DOI：10.16798/j. issn. 1003-0530. 2017. 08. 007Total Variability Modelling for Face Representation Learningwith CNN Multi-LayersHONG Xin-hai SONG Yan(National Engineering Laboratory of Speech and Language Information Processing, EEIS ofUniversity of Science and Technology of China, Hefei, Anhui 230027, China)Abstract ：Learning face representation is a crucial problem of face recognition system. Recently deep face representation based on convolutional neural networks (CNN)have achieved state-of-the-art performance on public LFW (Labeled Faces in the W ild). However, the deficiency of generalization and robustness still exists in complex datasets, such as IARPA Janus Benchmark A (IJB-A). In this paper, a total variability modelling (TVM) method utilizing different output layers of CNN for face representation is proposed. The variations of local deep features can be modeled in the total variability subspace, which effectively aggregates the local deep features into a compact embedding feature (iV ector). Evaluations on the IJB-A dataset show the proposed face representation learning method, compared with existed face deep representations, has achieved better performance and efficiency.Key words：convolutional neural networks；total variability modelling；face recognition；face representationi引言人脸识别是计算机视觉领域最重要的研究方向 之一，如何学习得到区分性和鲁棒性的人脸特征表达 是人脸识别的关键性问题。
深度学习论文汇总
深度学习论⽂汇总本博客⽤于记录⾃⼰平时收集的⼀些不错的深度学习论⽂，近9成的⽂章都是引⽤量3位数以上的论⽂，剩下少部分来⾃个⼈喜好，本博客将伴随着我的研究⽣涯长期更新，如有错误或者推荐⽂章烦请私信。
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一种改善光照对深度人脸识别影响的方法
收稿日期:2018-04-18 修回日期:2018-08-21 网络出版时间:2018-12-20基金项目:广东省高校重大科研项目-特色创新项目(自然科学)(2016KTSCX 167);广东省自然科学基金(2016A 030313384)作者简介:贺 辉(1979-),女,博士,副教授,研究方向为图像处理与智能分析;陈思佳(1995-),男,硕士研究生,研究方向为深度学习㊂网络出版地址:http :// /kcms /detail /61.1450.TP.20181220.1001.014.html一种改善光照对深度人脸识别影响的方法贺 辉,陈思佳,黄 静(北京师范大学珠海分校信息技术学院,广东珠海519087)摘 要:在人脸识别领域,消除光照变化的不利影响一直以来都是一个难以解决的问题㊂而与过去的机器学习模型不同,深度学习模型的结构具有和人类视觉神经结构相似的特性㊂这虽然使模型表现出了非常好的识别效果,但也使模型变得难以解释,以至于以往的人脸光照预处理方法不再可靠㊂考虑到卷积神经网络具有生物视觉神经的特点,文中在带彩色恢复的多尺度视网膜增强(MSRCR )方法的基础上,结合对比度增强处理,提出了一种类视网膜大脑皮层增强法,以改善基于深度学习的人脸识别模型中光照不均造成的错误识别问题㊂同时,与基于子空间统计的方法㊁基于光照不变表示的方法㊁基于直方图均衡化方法进行了多组对比实验,结果显示该方法比其他方法更有效,可使深度学习模型的识别率显著提高㊂关键词:人脸识别;深度学习;光照;视网膜大脑皮层增强中图分类号:TP 301 文献标识码:A 文章编号:1673-629X (2019)04-0038-04doi :10.3969/j.issn.1673-629X.2019.04.008An Improved Illumination Approach in Deep Face RecognitionHE Hui ,CHEN Si -jia ,HUANG Jing(School of Information Technology ,Beijing Normal University ,Zhuhai ,Zhuhai 519087,China )Abstract :It has always been a difficult problem to eliminate the adverse effects of varying illumination in face recognition.Different from existed machine learning models ,the structure of deep learning model is similar to that of human visual nerve.This makes the model show better recognition effect ,but also makes it difficult to explain ,so that the previous face illumination pretreatment method is no longer reliable.Therefore ,considering the convolutional neural network owning characteristics of biological visual nerve ,on the basis of multi -scale Retinex with color restoration (MSRCR ),combining contrast enhancement processing ,we propose a Retinex enhancement method to improve the error identification problem caused by uneven illumination in face recognition model based on deep learning.And com⁃pared with the methods based on subspace statistics ,illumination invariant representation and histogram equalization ,the results show that this method is more effective than other methods ,and can significantly improve the recognition rate of the deep learning model.Key words :face recognition ;deep learning ;illumination ;Retinex reinforcement0 引 言人脸识别一直以来都是计算机视觉领域的一个研究热点,相比指纹识别㊁虹膜识别等识别方式,人脸识别有更多优势,因此,基于人脸识别技术的应用也越来越广泛㊂随着深度学习的兴起,越来越多的领域采用深度学习模型作为主要模型,而在计算机视觉领域,卷积神经网络(convolutional neural network ,CNN )成为了最有效的模型之一,人脸识别也不例外,基于卷积神经网络分类模型的方法具有明显优于以往机器学习模型的效果[1]㊂神经网络如此强大的一个主要原因是深层神经网络拥有的 万有逼近”能力:深层神经网络可以逼近任意连续函数㊂而卷积神经网络具有强大的采样能力,能够自动提取图像集中的主要成分[2]㊂然而,虽然人脸识别率已经接近100%,但是市面上人脸识别设备的应用却很少,主要原因还是模型训练集远远不能覆盖现实中所有的影响因素,而在这些影响因素中,光照是最具代表性的一种㊂虽然卷积神经网络本身十分强大,在数据集足够好的时候可以几乎不采用任何图像预处理方式,但是当数据集不够全面,或者说缺少足够多的数据时,光照对识别率的影响很大㊂因此,改善光照对人脸识别的影响对实现人脸识别在工业上的应用有着极其重要的意义[3-4]㊂在光照问题上,近年来并没有提出与CNN 相结合的方法,主要原因是人为提取高质量的特征十分困难,第29卷 第4期2019年4月 计算机技术与发展COMPUTER TECHNOLOGY AND DEVELOPMENT Vol.29 No.4Apr. 2019并且人为干涉会降低模型提取到的特征的质量[5],因此现在的主流主张是让模型自主提取特征㊂例如,特征脸方法[6]是人脸识别领域内的经典方法,利用PCA (principal component analysis)方法计算多张人脸照片的协方差,并求出其特征值和特征向量,接着利用特征值保留最大的若干特征向量,最后利用特征向量对原图像进行投影,这样就达到了保留主成分而降维的目的;基于光照不变表示的方法[7-8],认为映射到人眼中的图像和光的长波(R)㊁中波(G)㊁短波(B)以及物体反射性质有关;局部二值模式法(local binary patterns, LBP)在人脸识别中应用广泛,对光照㊁年龄㊁表情等变化都有很强的鲁棒性[9-10],它通过与周围像素的对比,具有旋转不变性和灰度不变性等特点,但是经它处理后的图像并不符合直觉,换句话说,并不能轻易地由人眼分辨㊂实际上,经过LBP处理后的图像一般不直接用于识别,而是将区域分块直方图连成一个特征向量,放入分类器中做分类,显然这种方法并不适合与卷积神经网络相结合,因为它本身就是一种采样操作,降低了图片的可识别性㊂既然CNN具有生物视觉神经的特点,那么人为干涉实际上是可以提高模型提取特征的质量的,就像近视眼镜对于近视眼一样㊂基于这样的考虑,结合直方图均衡化预处理后图像的特点,文中提出了一种类视网膜大脑皮层增强法,并通过实验进行验证㊂1 算法描述1.1 类视网膜大脑皮层增强法(SRRM)视网膜-大脑皮层(Retinex)理论[7]认为世界是无色的,人眼看到的世界是光与物质相互作用的结果,也就是说,映射到人眼中的图像和光的长波(R)㊁中波(G)㊁短波(B)以及物体反射性质有关,如式1所示㊂I(x,y)=R(x,y)L(x,y)(1)其中,I是人眼中看到的图像;R是物体的反射分量;L是环境光照射分量;(x,y)是二维图像对应的像素位置㊂基于Retinex理论,有学者提出了SSR(single scale Retinex)方法[8],通过估算L来计算R,具体来说,L可以通过高斯模糊和I做卷积运算求得,如下: logR=log I-log L(2) L=F*I (3)其中,F是高斯模糊滤波器; *”表示卷积运算㊂通过选择不同的高斯周围空间常数(Gaussian sur⁃round space constant)对图像处理有比较大的影响,小的常数对细节和动态区域压缩有比较好的效果,但是整体色彩容易失真,大的常数反之,这也是SSR方法的不足之处㊂针对这个问题,有学者提出了MSR(multi-scale Retinex)方法[11-13],MSR使用了多种常数,并用权值的方法将它们混合在一起,如下:log R=∑N i=1w i log R i(4)Fi=12πσiexp(-r2σ2i)(5)其中,σi为高斯周围空间常数;w i为每个待混合图像的权值,一般来说:wi=1N (6)∑N i=1w i=1 (7)其中,N为选用高斯周围空间常数的数量㊂然而SSR和MSR对于色彩恢复在灰度上的都有些问题,主要原因在log R恢复到[0,255]色彩空间的方式,也就是恢复到R的方式㊂针对这个问题, Parthasarathy等提出了带色彩恢复的多尺度视网膜增强算法(multi-scale Retinex with color restoration, MSRCR)[14],如式8~10:I'i=Ii/∑S i=1I i(8)Ci=βlog(αI'i)(9)Ri=G(Ci log R-b)(10)其中,C i是色彩恢复函数;α㊁β㊁G都是经验参数;b是经验偏移量;S是色彩通道数㊂为从根本上消除MSRCR方法导致的图像关键点不明显的缺点,结合直方图均衡化在增强图像对比度上的优点,提出了一种类视网膜大脑皮层增强法(sim⁃ilar Retinex reinforcement method,SRRM)㊂SRRM方法同时克服了直方图均衡化方法导致的图像多处变化大的缺点,也即经过SRRM处理后的图像具有关键点外的变化度小和有利于目视判读的优点,也即该方法同时保留了灰度增强和视网膜大脑皮层法的优点㊂算法基本步骤如下:输入:人脸图像矩阵;输出:增强结果矩阵㊂Step1:将图像转为RGB图,并对图像利用MSRCR进行处理;Step2:将处理后的图像转为灰度图,进行直方图均衡化处理㊂1.2 基于深度学习的人脸识别分类器基于CNN的人脸识别分类器非常多,它们一次次地刷新了LFW的记录[1],甚至有些网络模型拥有非常好的鲁棒性,即便不对数据做过多处理也可以得到非常好的效果[15]㊂为验证文中提出的预处理方法的有效性,这里使用一种相对并不复杂的CNN结构㊂㊃93㊃ 第4期 贺 辉等:一种改善光照对深度人脸识别影响的方法将输入图片作为输入层;第二层是卷积层,卷积核尺寸为5×5,步长为1;第三层是池化层,池化核的尺寸为2×2,步长为2;第四层是卷积层,卷积核尺寸为5×5,步长为1;第五层是池化层,池化核的尺寸为2×2,步长为2;第六层是全连接层,神经元数量为256;最后一层也是全连接层,神经元数量为68,即训练集类别㊂将最后的输出结果输入到softmax函数中做分类㊂2摇实验及结果分析文中选用CMU_PIE人脸光照数据库作为实验数据集,CMU_PIE数据集中的Pose9是正脸居中裁剪好的人脸数据,一共包含1632张人脸图片,包含68个来自多个国家的人的人脸,其中每人有24张尺寸为64×64的灰度图片,包含3张暗光照下不同表情的图片和21张不同角度的环绕光照图片㊂为了保证数据集倾斜情况的发生,对每张人脸,分别取19张图片作为训练集,5张图片作为测试集,这样训练集有1292张图片,测试集有340张图片,训练集和测试集不相交㊂实验同时对比了文中提出的SR⁃RM方法与特征脸方法㊁LBP方法[16]㊁MSRCR方法㊁直方图均衡化方法分别对图片进行预处理后的CNN 的识别效果,对于每张图片,CNN每次会返回最有可能的预测结果,实验中根据分类器的识别率作为标准㊂为了保证实验结果的客观真实,对于每种图像处理方法的训练集和测试集,都进行了10次随机选取,对于每次选取的数据,又进行了10轮神经网络的训练,最终的实验结果是100组实验结果取均值,总体技术路线如图1所示㊂各种方法预处理实验结果如图2~图图1 总体技术路线图2 LBP处理(注:第一行是原图,第二行是处理后的图像)从图2可见,虽然LBP表现出了强大的人脸识别问题解决能力,但是经它处理后的图像并不符合直觉,换句话说,并不能轻易地由人眼分辨㊂最后的识别结果也表明LBP不适合与卷积神经网络结合,因为它本身就是一种采样操作,降低了图片的可识别性㊂从图3可见,MSRCR可以比较好地保留人脸轮廓,消去图中的光照和阴影与皮肤信息,但是对比度不高,一些轮廓细节不明显㊂由图4可见,直方图均衡化预处理也有明显的缺点:变化后的图像灰度级可能会减少,使某些细节不明显甚至消失;均衡化后的灰度范围取决于图3 MSRCR处理(注:第一行是原图,第二行是处理后的图像)图4 直方图均衡化处理(注:第一行是原图,第二行是处理后的图像)图5 SRRM处理(注:第一行是原图,第二行是经直方图均衡化处理后的图像,第三行是经MSRCR处理后的图像,第四行是经SRRM处理后的图像)㊃04㊃ 计算机技术与发展 第29卷原图像的灰度范围,因此对灰度范围过小的图像对比度增强的效果有限㊂而从图5中可以看出,SRRM方法同时保留了灰度增强和视网膜大脑皮层法的优点㊂需要特别说明的是,当预处理方法为PCA时,会先将数据集分为训练集和测试集,再让PCA模型对训练数据集拟合,最后再分别对训练集和测试集进行重构预处理,以此来避免预处理方法对测试集的拟合㊂表1 多种光照预处理方法与CNN结合后的实验结果预处理方法识别率/%无处理78.72PCA,90%主成分,无白化77.80PCA,90%主成分,白化78.53LBP56.07MSRCR71.58直方图均衡化84.13SRRM90.74 表1结果显示,SRRM方法相比其他图像预处理方法,在光照处理上拥有更好的效果,明显提升了CNN在光照影响环境下人脸识别的能力㊂3摇结束语提出了一种新的光照预处理方法:视网膜大脑皮层增强法(RRM),并与多种典型的光照预处理方法进行了对比实验㊂实验结果证明,该方法在处理光照不均图像并与CNN结合后的效果远超其他方法,有效地提升了CNN在不均匀光照环境下对人脸识别的能力㊂更重要的是,提出的CNN和以往的分类器不同,它的识别方式应该符合直觉,也就是说图片应该可以被人眼识别,并通过实验证明了这种想法的正确性,对解释CNN这个复杂的黑盒模型非常有帮助㊂对于比较极端的光照情况(如半张脸完全被黑暗覆盖),虽然该方法也有复原图像的能力,但是在一些细节上有比较大的瑕疵,针对这个问题,除了一些光照补偿算法外,可以考虑利用人脸的对称性复原人脸,从已经做过的实验结果来看这应该比子空间匹配更有利㊂参考文献:[1] LEARNED-MILLER E,HUANG G B,ROYCHOWDHU⁃RYA,et beled faces in the wild:a survey[M]//Ad⁃vances in face detection and facial image analysis.[s.l.]:Springer International Publishing,2016:189-248. 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[11]XIE Bin,GUO Fan,CAI Zixing.Improved single image de⁃hazing using dark channel prior and multi-scale retinex[C]//International conference on intelligent system designand engineering application.Changsha,China:IEEE,2010: 848-851.[12]RAHMAN Z U,JOBSON D J,WOODELL G A.Investiga⁃ting the relationship between image enhancement and imagecompression in the context of the multi-scale retinex[J].Journal of Visual Communication and Image Representation, 2011,22(3):237-250.[13]MENG Q,BIAN D,GUO M,et al.Improved multi-scale ret⁃inex algorithm for medical image enhancement[M]//Infor⁃mation engineering and applications.[s.l.]:[s.n.],2012: 930-937.[14]PARTHASARATHY S,SANKARAN P.An automated multiscale retinex with color restoration for image enhancement[C]//National conference on communications.Kharagpur,India:IEEE,2012:1-5.[15]GOODFELLOW I J,POUGETABADIE J,MIRZA M,et al.Generative adversarial networks[J].Advances in Neural In⁃formation Processing Systems,2014,3:2672-2680. 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基于深度学习的人脸妆容迁移算法
收稿日期：２０２００４０７；修回日期：２０２００６０５ 基金项目：国家自然科学基金青年基金资助项目（１１７０５１０３）；山东省重点研发计划资助 项目（２０１９ＧＧＸ１０５０１３）；山东省高 等 学 校 公 共 安 全 管 理 技 术 重 点 实 验 室 （山 东 管 理 学 院）资 助 项 目；山 东 管 理 学 院 科 研 启 航 计 划 资 助 项 目 （ＱＨ２０２０Ｚ０８）
为了进一步实现人脸妆容自动迁移技术，避免现有妆容迁 移方法没有充分考虑人与人的五官差异而导致的迁移脸部结 构丢失等问题，本文提出了一种基于深度卷积神经网络的人脸 妆容迁移算法。
１ 人脸妆容区域自主定位
由于图像信息是逐 像 素 传 输 的 ［１８］，所 以 在 进 行 人 脸 妆 容 自动迁移之前，需实现人脸图像和示例妆容图像的妆容区域自 主检测与定位。
肤细节层和色彩层三层；然后便可将信息从妆容示例图像中的
每一层传递到目标人像图像的相应层。
２ 基于深度卷积神经网络的妆容迁移网络
整个基于深度卷积神经网络的妆容迁移网络包含妆容传 递网络和损失网络两个部分。妆容传递网络实现了人像图像 和参考妆容之间的直接映射，然后将妆容化人像图像输入到损 失网络，根据输入的内容和参考妆容计算内容和样式损失，妆 容传递网络的权重将根据使用梯度下降算法在损失网络中计 算的损失进行更新。妆容传递网络是由多个卷积层和残差块 组成的深度卷积神经网络，妆容传递网络中每一层的权重是随 机初始化的，而损失网络则采用预训练的 ＶＧＧ１９网络的前几 个卷积层。整个网络结构和操作流程如图 ２所示。
０ 引言
图像风格迁移也称为图像风格转换，是指将输入图像的风 格转变成指定一幅或多幅图像风格的方法。在图像风格迁移 中，目标是在对新图像处理的同时保持原图像的纹理或风格。 图像风格迁移技术当前被广泛应用于移动相机滤镜、艺术图像 生成等图像 处 理 领 域。 随 着 手 机、便 捷 式 智 能 摄 影 设 备 的 发 展，作为其中人像处理软件的核心———人脸妆容迁移技术，当 前备受关注，诸如目前市面上商业化软件美图秀秀、ＴＡＺＺ等。 这些软件系统需要用户手动操作，并且只向用户提供一定数量 的固定化妆风格。然而智能人脸妆容迁移技术，可将参考妆容 迁移到素颜人脸上，并保持其上妆风格，使其达到在保持脸部 结构不变的同时尽可能地展现参考妆容的风格。但由于很难 明确地分离和表示图像的内容和风格，目前人脸妆容自动迁移 技术仍是一项挑战［１，２］。
deepfacelab原理
deepfacelab原理
Deepfacelab是一种基于神经网络技术的深度学习算法，其原理是通过模拟人脸的关键特征点，实现对人脸进行图像处理和合成。
这种
技术可以用于实现人脸换脸、表情合成、变老、变年轻等各种效果。
Deepfacelab的原理主要分为两部分：一是人脸对齐，二是人脸重建。
人脸对齐是指通过计算不同人脸之间的特征点的位置，将不同人
脸对齐，使得它们具有相同的几何形状和结构。
这一步是非常重要的，因为只有对齐后的人脸才能进行后续的处理。
人脸重建是指将已经对齐的人脸图像，通过深度学习神经网络进
行训练和学习，得到一个能够对人脸进行表情合成、面部移形、年龄
和性别转换等操作的模型。
这个模型可以根据不同的需求，对不同的
人脸进行处理，实现现实中无法想象的各种效果。
Deepfacelab的实现主要依靠以下技术：
1.深度学习算法：利用深度学习算法进行图像处理并进行模型训练，从而实现对人脸进行图像合成。
2.关键点检测：通过检测人脸图像中的各个关键点，比如眉毛、
眼睛、鼻子、嘴巴等，来完成人脸对齐。
3.GAN网络：利用生成对抗神经网络技术，将已有的人脸图像合成成新的人脸图像，并具备更好的真实性和良好的效果。
4.迭代法：通过不断的训练和迭代，不断优化模型的效果和精度。
总的来说，Deepfacelab基于神经网络技术和深度学习算法，利用关键点检测和生成对抗神经网络等技术手段，实现了对人脸图像进行
高质量合成的目的。
同时，该技术还可以用于提升面部识别和人脸图
像处理的效果，对于深入挖掘人脸图像的特征和属性，为人类的科学
研究提供了有力的支持。
Deep Learning 教程中文版
。在上一个教程中我们扩展了
的定义，使其包含向量运
算，这里我们也对偏导数
也做了同样的处理（于是又有
Hale Waihona Puke ）。那么，反向传播算法可表示为以下几个步骤：
1. 进行前馈传导计算，利用前向传导公式，得到 2. 对输出层（第 层），计算：
直到输出层 的激活值。
3. 对于
的各层，计算：
4. 计算最终需要的偏导数值：
实现中应注意：在以上的第2步和第3步中，我们需要为每一个 值计算其 且我们已经在前向传导运算中得到了 。那么，使用我们早先推导出的
[注：通常权重衰减的计算并不使用偏置项 ，比如我们在
的定义中就没有使用。一般来说，将偏置项包
含在权重衰减项中只会对最终的神经网络产生很小的影响。如果你在斯坦福选修过CS229（机器学习）课程，或者在 YouTube上看过课程视频，你会发现这个权重衰减实际上是课上提到的贝叶斯规则化方法的变种。在贝叶斯规则化方法 中，我们将高斯先验概率引入到参数中计算MAP（极大后验）估计（而不是极大似然估计）。]
括
的输出值。之后，针对第 层的每一个节点 ，我们计算出其“残差” ，该残差表明了该节点对最终
输出值的残差产生了多少影响。对于最终的输出节点，我们可以直接算出网络产生的激活值与实际值之间的差距，我们
将这个差距定义为
（第 层表示输出层）。对于隐藏单元我们如何处理呢？我们将基于节点（译者注：第
层节点）残差的加权平均值计算 ，这些节点以 作为输入。下面将给出反向传导算法的细节：
2 /4
1 3 -3 -2 2
-U fldl
以上的逐步反向递推求导的过程就是“反向传播”算法的本意所在。] 4. 计算我们需要的偏导数，计算方法如下：
人脸识别实训学习总结深度学习与人脸识别技术
人脸识别实训学习总结深度学习与人脸识别技术人脸识别技术近年来得到了广泛的应用和深入的研究，它在安防、金融、教育等领域有着重要的作用。
作为一种通过计算机分析和识别人脸图像来确定身份的技术，人脸识别借助于深度学习算法的发展，不断提升着准确度和鲁棒性。
在本次人脸识别实训中，我深入学习了深度学习与人脸识别技术，并将总结和分享我的学习心得。
首先，在学习过程中，我了解到了深度学习技术在人脸识别中的应用。
深度学习是一种以人工神经网络为基础的机器学习方法，通过多个神经网络层的建模来实现对复杂数据的学习和分析。
在人脸识别中，深度学习可以通过构建深层架构的卷积神经网络（CNN）来对图像进行特征提取和分类，不仅提高了识别的准确度，还具备了较好的鲁棒性，可以在复杂环境下进行有效的人脸识别。
其次，我学习了一些常用的人脸识别算法和模型。
其中，卷积神经网络（CNN）是人脸识别中应用最广泛的算法之一。
它通过卷积、池化和全连接层等操作，实现特征的提取和分类。
此外，基于深度学习的人脸识别算法还包括了人脸检测、关键点定位和人脸对齐等模块。
这些算法和模型的学习帮助我更加全面地了解了人脸识别的基本原理和技术流程。
在实训中，我还学习了如何搭建和训练人脸识别模型。
首先，我了解了常用的深度学习框架，如TensorFlow和PyTorch等，并学习了如何使用这些框架进行深度学习模型的构建。
其次，我学习了数据的预处理和增强方法，以减少数据噪声和提高模型的鲁棒性。
最后，我通过在实际数据集上进行实验和调参，不断优化模型的表现。
这一系列的学习和实践使我对人脸识别技术有了更深入的理解和应用能力。
通过本次实训，我不仅学到了深度学习与人脸识别技术的相关知识，还锻炼了自己的动手能力和解决问题的能力。
在实验过程中，我遇到了许多挑战和困难，如数据集的采集和清洗、模型的调试和优化等。
但是经过不断的努力和尝试，我逐渐掌握了解决问题的方法和技巧。
同时，我也通过与同学的合作和交流，拓宽了自己的视野，丰富了人脸识别技术的应用领域和前沿动态。
deepfacelive 原理
深度假脸技术是近年来人工智能领域备受关注的一个热门话题。
该技术利用深度学习算法和计算机视觉技术，可以实现将一个人的脸部特征以及表情生动地叠加到另一个人的面部上，从而实现高度逼真的仿真效果。
而这一技术的背后，其原理涉及到多领域的知识和技术，包括人脸识别、深度学习、图像处理等领域。
本文将深入探讨deepfacelive技术的原理，以期帮助读者深入了解这一引人注目的技术的工作原理和实现方式。
一、深度学习在deepfacelive中的应用深度学习技术是deepfacelive实现高度仿真效果的关键。
通过构建深度神经网络模型，模型可以自动学习和提取输入图像中的人脸特征，并将这些特征映射到目标图像上。
这一过程是通过大量标注的训练数据来实现的，模型根据输入图像和目标图像之间的差异，不断调整网络参数，最终实现人脸表情的高度逼真的叠加效果。
二、人脸识别技术在deepfacelive中的作用人脸识别技术是deepfacelive实现仿真效果的基础。
通过人脸识别技术，系统可以准确地识别输入图像中的人脸，并提取关键的面部特征。
在将这些特征叠加到目标图像上时，系统需要确保叠加位置和姿态的准确性，从而实现高度逼真的仿真效果。
三、深度神经网络的结构与参数设计深度神经网络的结构设计和参数调整对于deepfacelive的性能和效果起着至关重要的作用。
在设计网络结构时，需要考虑到深度、宽度、以及连接方式等因素，从而实现对输入图像和目标图像的有效表征和映射。
在参数调整方面，需要通过大量的实验和验证，找到最优的参数设置，以实现系统在不同场景下的高效性能和稳定性。
四、数据集的构建与标注数据集的构建和标注是deepfacelive实现高效仿真效果的基础。
通过大量的人脸图像数据，系统可以学习到人脸的各种表情和特征，并根据输入图像的特征，自动计算叠加的位置和姿态。
在进行标注时，需要考虑到人脸的姿态、光照、表情等因素，以提高系统对各种场景的适应能力。
face_recognition库原理
face_recognition库原理Face_recognition是一个用于人脸识别的Python库，其基本原理是使用深度学习模型来提取和比较人脸特征。
下面我们将详细介绍face_recognition库的原理。
1.人脸检测：Face_recognition库使用HOG（Histogram of Oriented Gradients，方向梯度直方图）算法进行人脸检测。
HOG算法通过计算图像局部区域的梯度直方图来获得图像的特征向量，然后使用滑动窗口的方法来检测人脸。
2.人脸对齐：在进行人脸识别之前，需要对人脸进行对齐，使得不同人脸的特征点对应位置相同。
为了实现人脸对齐，face_recognition库使用了dlib库中的正交距离变换（Orthogonal Procrustes Analysis）算法。
该算法通过计算两组特征点之间的旋转、缩放和平移变换，使得两组特征点对应的点之间的欧式距离最小。
3.人脸特征提取：Face_recognition库基于深度学习模型的思想，使用预训练的卷积神经网络（Convolutional Neural Networks，CNN）来提取人脸特征。
具体来说，它使用了dlib库中的基于ResNet的深度学习模型。
该模型可以将人脸图像映射到一个128维的特征向量，这个特征向量被称为人脸嵌入（face embedding）或人脸特征向量。
4.人脸比较：在进行人脸识别时，face_recognition库将两个人脸的特征向量进行比较，通过计算两个特征向量之间的欧式距离来判断其相似度。
欧式距离越小，说明两个人脸越相似。
5.人脸识别：总的来说，face_recognition库的原理是使用深度学习模型来提取和比较人脸特征，通过计算特征向量之间的欧式距离来判断人脸的相似度，从而实现人脸检测和识别的功能。
这个库在准确性和速度上都具有较高的性能，因此被广泛应用于人脸识别系统中。
基于深度学习技术的人脸识别研究
基于深度学习技术的人脸识别研究摘要：人脸识别技术在现代生活中得到了广泛应用。
本文基于深度学习技术，研究了人脸识别技术的相关算法与模型，包括卷积神经网络、人脸识别特征提取、人脸识别分类等方面。
通过实验验证，本文提出的基于深度学习技术的人脸识别模型具有较高的准确率和稳定性，可以应用于实际场景中。
关键词：深度学习；人脸识别；卷积神经网络；特征提取；分类Abstract:Face recognition technology has been widely used in modern life. Based on deep learning technology, this paper studies the related algorithms and models of face recognition technology, including convolutional neural network, face recognition feature extraction, face recognition classification and other aspects. Through experiments, the face recognition model based on deep learning technology proposed in this paper has high accuracy and stability, which can be applied to practical scenarios.Keywords: Deep learning; face recognition; convolutional neural network; feature extraction; classification1. 引言随着科学技术的不断发展，人脸识别技术已经成为了现代生活中不可或缺的一部分。
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2Department of Electronic Engineering, The Chinese University of Hong Kong
3Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences
sy011@.hk
xgwang@.hk
xtang@.hk
Abstract
The key challenge of face recognition is to develop effective feature representations for reducing intra-personal variations while enlarging inter-personal differences. In this paper, we show that it can be well solved with deep learning and using both face identification and verification signals as supervision. The Deep IDentification-verification features (DeepID2) are learned with carefully designed deep convolutional networks. The face identification task increases the inter-personal variations by drawing DeepID2 extracted from different identities apart, while the face verification task reduces the intra-personal variations by pulling DeepID2 extracted from the same identity together, both of which are essential to face recognition. The learned DeepID2 features can be well generalized to new identities unseen in the training data. On the challenging LFW dataset [11], 99.15% face verification accuracy is achieved. Compared with the best deep learning result [21] on LFW, the error rate has been significantly reduced by 67%.
1
signal, its DeepID2 features are extracted in the top hidden layer of the learned hierarchical nonlinear feature representation, and then mapped to one of a large number of identities through another function g(DeepID2). In the testing stage, the learned DeepID2 features can be generalized to other tasks (such as face verification) and new identities unseen in the training data. The identification supervisory signal tend to pull apart DeepID2 of different identities since they have to be classified into different classes. Therefore, the learned features would have rich identity-related or interpersonal variations. However, the identification signal has a relatively weak constraint on DeepID2 extracted from the same identity, since dissimilar DeepID2 could be mapped to the same identity through function g(·). This leads to problems when DeepID2 features are generalized to new tasks and new identities in test where g is not applicable anymore. We solve this by using an additional face verification signal, which requires that every two DeepID2 vectors extracted from the same identity are close to each other while those extracted from different identities are kept away. The strong per-element constraint on DeepID2 can effectively reduce the intra-personal variations. On the other hand, using the verification signal alone (i.e. only distinguishing a pair of DeepID2 at a time) is not as effective in extracting identity-related features as using the identification signal (i.e. distinguishing thousands of identities at a time). Therefore, the two supervisory signals emphasize different aspects in feature learning and should be employed together.
Deep Learning Face Representation by Joint Identification-Verification
arXiv:1406.4773v1 [cs.CV] 18 Jun 2014
Yi Sun1
Xiaogang Wang2
Xiaoou Tang1,3
1Department of Information Engineering, The Chinese University of Hong Kongame identity could look much different when presented in different poses, illuminations, expressions, ages, and occlusions. Such variations within the same identity could overwhelm the variations due to identity differences and make face recognition challenging, especially in unconstrained conditions. Therefore, reducing the intra-personal variations while enlarging the inter-personal differences is an eternal topic in face recognition. It can be traced back to early subspace face recognition methods such as LDA [1], Bayesian face [17], and unified subspace [23, 24]. For example, LDA approximates inter- and intra-personal face variations by using two linear subspaces and finds the projection directions to maximize the ratio between them. More recent studies have also targeted the same goal, either explicitly or implicitly. For example, metric learning [6, 9, 15] maps faces to some feature representation such that faces of the same identity are close to each other while those of different identities stay apart. However, these models are much limited by their linear nature or shallow structures, while inter- and intra-personal variations are complex, highly nonlinear, and observed in high-dimensional image space.