A face authentication scheme based on Affine-SIFT (ASIFT) and structural similarity (SSIM)

Authors:
Lifang Wu;Peng Zhou;Shuqin Liu;Xiuzhen Zhang;Emanuele Trucco
Affiliations:
School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;School of Electronic Information and Control Engineering, Beijing University of Technology, Beijing, China;School of CS&IT, RMIT University, Melbourne, VIC, Australia;School of Computing, University of Dundee, Dundee, The United Kingdom
Venue:
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Year:
2012

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Abstract

In this paper, we propose a novel face authentication approach based on affine scale invariant feature transform (ASIFT) and structural similarity (SSIM). The ASIFT descriptor defines key points which are used to match the gallery and probe face images. The matched pairs of key points are filtered based on the location of points in the gallery face image. Then the similarity between sub-images at a preserved pair of matched points is measured by Structural Similarity (SSIM). A mean SSIM (MSSIM) at all pairs of points is computed for authentication. The proposed approach is tested on FERET, CMU-PIE and AR databases with only one image for enrollment. Comparative results on the AR database show that our approach outperforms state-of-the-art approaches.