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Feature Extraction Research on the Face Recognition Process

Author: ZhaoDongJuan
Tutor: LiangJiuZuo
School: Jiangnan University
Course: Computer Software and Theory
Keywords: Face Recognition Feature Extraction Wavelet Transform Class augmented PCA Fuzzy set theory
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 90
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Abstract


Feature extraction for high-dimensional data classification has important significance, is widely used. The purpose of feature extraction is to extract a set of features , so that the dimensions of these features than the dimension of the original data is low, and maintaining data category properties. Instead of the original with the extracted feature data for various processing , the number of features to make the calculation of the classification process is reduced, and because the data related to the addition to the features to improve the classification efficiency. This article includes the following aspects: ( 1 ) and adaptive wavelet fusion class augmented PCA face recognition . The method first used discrete wavelet transform image compression on the human face , the low frequency components extracted from human faces , and then augmented by adaptive class PCA method of wavelet transform low-frequency component of the human face feature extraction , so as to achieve further dimension reduction purposes. Unlike class augmented PCA, this method does not require the information to build the sample between the classes , use more flexible , but also because the wavelet transform for image preprocessing, recognition rate and time-consuming algorithms have also been further optimized . ( 2 ) fusion of 2DPCA and fuzzy 2DLDA face recognition . This method first 2DPCA for face image processing , and then the fuzzy K-nearest neighbor algorithm to compute image membership matrix , and its integration into the 2DLDA process , resulting in blurred between-class scatter matrix and fuzzy within-class scatter matrix . And (2D) ~ 2PCALDA , the algorithm takes advantage of (2D) ~ 2PCALDA advantages to effectively extract the row and column identification information , taking into account the distribution information of the sample . ( 3 ) two-dimensional augmented PCA first class with 2DPCA directly face image feature extraction matrix , followed by the extracted features normalized , and then normalized after treatment characteristics and category information combined into class augmented data Finally the class 2DPCA augmented data processing to extract the final features. The algorithm only retains the structure of the face image information , and class information of the sample considered , so that the recognition rate has been greatly improved.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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