Face Image Feature Extraction and Feature Selection

Abstract:

In order to solve the problem of face recognition, the method of feature extraction and feature selection is presented in this paper. First using Gabor filters and face image as the convolution Operator to extract the Gabor feature vector of the image and also to uniform sampling; then using the PCA + LDA method to reduce the dimension for high-dimensional Gabor feature vector; Finally, using the nearest neighbor classifier to discriminate and determine the identity of a face image. The result I get is that the sampled Gabor feature in high-dimensional space can be projected onto low-dimensional space though the method of feature selection and compression. The new and original in this paper is that the method of PCA + LDA overcomes the problem of the spread matrix singular in the class and matrix too large which is brought by directly use the LDA.

• Periodical: Applied Mechanics and Materials (Volume 432)
• Pages: 587-591
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.432.587
• Online since: September 2013
• Authors: Yang Meng Tian, Yu Duo Zheng, Wei Jin, Gai Hong Du
• Keywords: Feature Extraction, Feature Selection, PCA + LDA Method

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