Affine Invariant Local Descriptors for Face Recognition


KIPS Transactions on Software and Data Engineering, Vol. 3, No. 9, pp. 375-380, Sep. 2014
10.3745/KTSDE.2014.3.9.375,   PDF Download:

Abstract

Under controlled environment, such as fixed viewpoints or consistent illumination, the performance of face recognition is usually high enough to be acceptable nowadays. Face recognition is, however, a still challenging task in real world. SIFT(Scale Invariant Feature Transformation) algorithm is scale and rotation invariant, which is powerful only in the case of small viewpoint changes. However, it often fails when viewpoint of faces changes in wide range. In this paper, we use Affine SIFT (Scale Invariant Feature Transformation; ASIFT) to detect affine invariant local descriptors for face recognition under wide viewpoint changes. The ASIFT is an extension of SIFT algorithm to solve this weakness. In our scheme, ASIFT is applied only to gallery face, while SIFT algorithm is applied to probe face. ASIFT generates a series of different viewpoints using affine transformation. Therefore, the ASIFT allows viewpoint differences between gallery face and probe face. Experiment results showed our framework achieved higher recognition accuracy than the original SIFT algorithm on FERET database.


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Cite this article
[IEEE Style]
Y. B. Gao and H. J. Lee, "Affine Invariant Local Descriptors for Face Recognition," KIPS Transactions on Software and Data Engineering, vol. 3, no. 9, pp. 375-380, 2014. DOI: 10.3745/KTSDE.2014.3.9.375.

[ACM Style]
Yong Bin Gao and Hyo Jong Lee. 2014. Affine Invariant Local Descriptors for Face Recognition. KIPS Transactions on Software and Data Engineering, 3, 9, (2014), 375-380. DOI: 10.3745/KTSDE.2014.3.9.375.