Abstract
Affine Scale Invariant Feature Transform (ASIFT) is robust to scales, rotation, scaling and affine transformation. It could be used for face recognition with pose variation. However, ASIFT requires large data. Could we reduce the data of ASIFT and preserve the face recognition performance? In this paper, we propose an effective face recognition algorithm to combining the structural similarity (SSIM) and PCA-ASIFT (PCA-ASIFT&SSIM).First, we reduce ASIFT dimension using principal component analysis and get PCA-ASIFT. The PCA-ASIFT’s discriminative capability drops because of the dimension reduction. It brings about more false SIFT matching. We further introduce the SSIM to reduce the false matching. The experimental results show the efficiency of the proposed approach.
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Qi, W., Hou, Y., Wu, L., Xu, X. (2014). A Pose Robust Face Recognition Approach by Combining PCA-ASIFT and SSIM. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_18
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DOI: https://doi.org/10.1007/978-3-319-12484-1_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
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