文章基本信息

标题：A Principle Factor Analysis Based on Euclidean Distance With Normalization Techniques for Illumination of Invariant Face Recognition
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作者：C.V.Arulkumar ; S.Sampath Kumar ; S.Vignesh 等
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2013
卷号：4
期号：3
页码：518-522
语种：English
出版社：Ayushmaan Technologies
摘要：Changes in lighting condition impacts the appearance of faces to a large extent. The work presented here compares the performance of the illumination compensation methods of face images like DCT normalization, Wavelet Denoising, Gradient Faces, Local Contrast Enhancement, and Weber Faces under different lighting conditions. The face images are preprocessed and normalized by each method to reduce the effect of illumination. Then the features of these preprocessed images are extracted using Principal factor Analysis (PFA) and the recognition is based on Euclidean Distance. In this paper, the advantages and drawbacks of each method are analyzed. The recognition rate and computational time of these methods are compared using Extended Yale B database.
关键词：Discrete Cosine Transform (DCT);Wavelet Denoising;Gradient Domain;Weber’s Law