三维离散余弦变换与反变换

新疆农业大学英文文献翻译

题目: C/C++实现BMP图像的三维离散余弦变换与反变换

姓名: 刘小翠

学院: 计算机与信息工程学院专业: 计算机科学与技术班级: 计科112班

学号: 114632215

指导教师: 杨树媛职称: 助教

2014年12月30日

新疆农业大学教务处制

FACE RECOGNITION USING DISCRETE COSINE TRANSRORM AND FUZZY LINEAR DISCRIMINANT ANALYSIS

Qi-wen ZHANG, Wen-xia DU, Liu-qing YUAN, Ming LI

School of Computer and Communication

Lanzhou University of Technology

Lanzhou, China

qingwenzhang@

Abstract : In order to solve the problem of uncertainty that occurred in the field of face recognition because of the variations of the facial expressions, illuminations and poses as well, a new feature extraction method for face recognition is proposed in this paper. The method combines the DCT with FLDA. Firstly, DCT is performed on the entire face image to obtain all the components in the frequency domain. Due to the energy compaction, only the lower frequency components will be retained and thus

the dimension reduced features are obtained, then FLDA is employed to extract the most discriminating features. Finally, the nearest neighbor classifier is employed for classification. The results of experiments conducted on Olivetti Research Laboratory (ORL) database show that the proposed method is better than other methods in terms

of accurate recognition rate.

Keywords :Face Recognition; Feature Extraction; Discrete Cosine Transform; Fuzzy Linear Discriminant Analysis.

I. INTRODUCTION

Face recognition is a challenge task in bio metrics, pattern recognition field and computer vision communities. It has been an active research area in the past few years because of its potential applications in areas such as security systems, identify authentication, video telephony, medicine, and so on. In face recognition, feature extraction is one of the most important steps, and it performs the reduction of high dimensional image data into low dimensional feature vectors. The popular feature extraction techniques most frequently used with face recognition are principal component analysis (PCA), linear discriminant analysis (LDA), independent component analysis (ICA), and so on. These methods extract features in image domain. In recent years, some researchers have investigated the possibility of extracting features in frequency domain, such as the space obtained by performing discrete cosine transform (DCT) [1,2,3]. The results have shown that it is feasible and promising to extract discriminatory frequency components for classification. Ha fed and Levine [1] used DCT for face recognition. They pointed out that DCT obtains the