Exploring deep features with different distance measures for still to video face matching

Abstract

Still to video (S2V) face recognition attracts many interests for researchers in computer vision and biometrics. In S2V scenarios, the still images are often captured with high quality and cooperative user condition. On the contrary, video clips usually show more variations and of low quality. In this paper, we primarily focus on the S2V face recognition where face gallery is formed by a few still face images, and the query is the video clip. We utilized the deep convolutional neural network to deal with the S2V face recognition. We also studied the choice of different similarity measures for the face matching, and suggest the more appropriate measure for the deep representations. Our results for both S2V face identification and verification yield a significant improvement over the previous results on two databases, i.e., COX-S2V and PaSC.