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Recent years, face recognition based on video has been concerned by more and more persons. At the same time, 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 classc 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 wtth 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 Yale and ORL Database suggest that proposed method can achieve lower error rates in face recognition task. [ABSTRACT FROM AUTHOR]