Using the visual Words based on Affine-SIFT descriptors for face recognition

Video-based face recognition has drawn a lot of attention in recent years. On the other hand, Bag-of-visual Words (BoWs) representation has been successfully applied in image retrieval and object recognition recently. In this paper, a video-based face recognition approach which uses visual words is proposed. In classic visual words, Scale Invariant Feature Transform (SIFT) descriptors of an image are firstly extracted on interest points detected by difference of Gaussian (DoG), then k-means-based visual vocabulary generation is applied to replace these descriptors with the indexes of the closet visual words. However, in facial images, SIFT descriptors are not good enough due to facial pose distortion, facial expression and lighting condition variation. In this paper, we use Affine-SIFT (ASIFT) descriptors as facial image representation. Experimental results on UCSD/Honda Video Database and VidTIMIT Video Database suggest that visual words based on Affine-SIFT descriptors can achieve lower error rates in face recognition task.