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21 October 2015 SIFT fusion of kernel eigenfaces for face recognition
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Abstract
In this paper, we investigate an application that integrates holistic appearance based method and feature based method for face recognition. The automatic face recognition system makes use of multiscale Kernel PCA (Principal Component Analysis) characterized approximated face images and reduced the number of invariant SIFT (Scale Invariant Feature Transform) keypoints extracted from face projected feature space. To achieve higher variance in the inter-class face images, we compute principal components in higher-dimensional feature space to project a face image onto some approximated kernel eigenfaces. As long as feature spaces retain their distinctive characteristics, reduced number of SIFT points are detected for a number of principal components and keypoints are then fused using user-dependent weighting scheme and form a feature vector. The proposed method is tested on ORL face database, and the efficacy of the system is proved by the test results computed using the proposed algorithm.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Dakshina R. Kisku, Massimo Tistarelli, Phalguni Gupta, and Jamuna K. Sing "SIFT fusion of kernel eigenfaces for face recognition", Proc. SPIE 9652, Optics and Photonics for Counterterrorism, Crime Fighting, and Defence XI; and Optical Materials and Biomaterials in Security and Defence Systems Technology XII, 96520O (21 October 2015); https://doi.org/10.1117/12.2190205
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