A Benchmark and Comparative Study of Video-Based Face Recognition on COX Face Database

Abstract

Face recognition with still face images has been widely studied, while the research on video-based face recognition is inadequate relatively, especially in terms of benchmark datasets and comparisons. Real-world video-based face recognition applications require techniques for three distinct scenarios: 1) Videoto-Still (V2S); 2) Still-to-Video (S2V); and 3) Video-to-Video (V2V), respectively, taking video or still image as query or target. To the best of our knowledge, few datasets and evaluation protocols have benchmarked for all the three scenarios. In order to facilitate the study of this specific topic, this paper contributes a benchmarking and comparative study based on a newly collected still/video face database, named COX(1) Face DB. Specifically, we make three contributions. First, we collect and release a largescale still/video face database to simulate video surveillance with three different video-based face recognition scenarios (i.e., V2S, S2V, and V2V). Second, for benchmarking the three scenarios designed on our database, we review and experimentally compare a number of existing set-based methods. Third, we further propose a novel Point-to-Set Correlation Learning (PSCL) method, and experimentally show that it can be used as a promising baseline method for V2S/S2V face recognition on COX Face DB. Extensive experimental results clearly demonstrate that video-based face recognition needs more efforts, and our COX Face DB is a good benchmark database for evaluation.

An Extended Sparse Classification Framework for Domain Adaptation in Video Surveillance

“…Fig 2(a) shows ROIs of 5 selected target individuals and their test video correspondence is recorded with 3 cameras above different portals. COX-S2V [37] consists of 1000 subjects, where each subject has a high quality still images under controlled conditions, and four lower-quality facial trajectories captured under uncontrolled conditions. Each trajectory has 25 faces, where ROIs taken from these videos encounter changes in illumination, expression, scale, viewpoint, and blur.…”

A paired sparse representation model for robust face recognition from a single sample

“…In order to evaluate the performance of the proposed S+V model for still-to-video FR, an extensive series of experiments are conducted on Chokepoint 2 [24] and COX-S2V 3 [25] datasets. Chokepoint [24] and COX-S2V [25] datasets are suitable for ex-…”

“…Moreover, since most state-of-the-art FR methods rely on Convolution Neural Network (CNN) architectures such as ResNet [20] and VGGNet [21], the model is fed with CNN features extracted from the atoms of dictionaries [22,23], in order to further improve still-tovideo FR accuracy. Performance of the SRC implementation is evaluated on two public video FR databases -Chokepoint [24] and COX-S2V [25].…”

Attributes in Multiple Facial Images

“…There are 293 identities from PaSC testing dataset, including 9376 still images (about 32 images per subject), and 2802 videos (approximately ten videos per person and 100 frames per video). Another dataset is COX [25], which has 1,000 subjects, 3 videos captured for each subject with 3 different camcorders. An interactive tool for annotating facial attributes was developed, which displayed multiple face images from the same subject.…”

“…The COX [25] consists of 1,000 subjects and three videos for each subject. We focus on the videos, which contain several frames, and demonstrate the attribute inconsistency issue.…”