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Proceedings Paper

Image preprocessing study on KPCA-based face recognition
Author(s): Xuan Li; Dehua Li
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Paper Abstract

Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

Paper Details

Date Published: 14 December 2015
PDF: 8 pages
Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 981306 (14 December 2015); doi: 10.1117/12.2203655
Show Author Affiliations
Xuan Li, Huazhong Univ. of Science and Technology (China)
Dehua Li, Huazhong Univ. of Science and Technology (China)

Published in SPIE Proceedings Vol. 9813:
MIPPR 2015: Pattern Recognition and Computer Vision
Tianxu Zhang; Jianguo Liu, Editor(s)

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