Proceedings ArticleDOI
Video-Based Face Association and Identification
Ching-Hui Chen,Jun-Cheng Chen,Carlos D. Castillo,Rama Chellappa +3 more
- pp 149-156
TLDR
Experimental results on the newly released JANUS challenge set 3 (JANUS CS3) dataset show that the proposed target face association (TFA) technique generates robust representations from target-annotated videos and demonstrates good performance for the task of video-based face identification problem.Abstract:
In this paper, we present a new video-based face identification algorithm, where the target (i.e., person of interest) in the probe video is only annotated once with a face bounding box in a frame and the video may consist of multiple shots. Most video face identification techniques assume that the video is of single shot, and thus the bounding boxes of the target face can be extracted by tracking a face across the video frames. Nevertheless, such automatic annotation is vulnerable to the drifting of the face tracker, and the face tracking algorithm is inadequate to associate the face images of the target across multiple shots. In this paper, we propose a target face association (TFA) technique that retrieves a set of representative face images in a given video that are likely to have the same identity as the target face. These face images are then utilized to construct a robust face representation of the target face for searching the corresponding subject in the gallery. Since two faces that appear in the same video frame cannot belong to the same person, such cannot-link constraints are utilized for learning a target-specific linear classifier for establishing the intra/inter-shot face association of the target. Experimental results on the newly released JANUS challenge set 3 (JANUS CS3) dataset show that our method generates robust representations from target-annotated videos and demonstrates good performance for the task of video-based face identification problem.read more
Citations
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Journal ArticleDOI
An Automatic System for Unconstrained Video-Based Face Recognition
Jingxiao Zheng,Rajeev Ranjan,Ching-Hui Chen,Jun-Cheng Chen,Carlos D. Castillo,Rama Chellappa +5 more
TL;DR: A robust and efficient system for unconstrained video-based face recognition, which is composed of modules for face/fiducial detection, face association, and face recognition is proposed.
Proceedings ArticleDOI
Face and Body Association for Video-Based Face Recognition
TL;DR: To track and associate subjects that appear across frames in multiple shots, this work solves a data association problem using both face and body appearance and shows up to 5% improvement in the identification rate over the state-of-the-art.
Posted ContentDOI
Face Identification and Clustering
TL;DR: This thesis studies the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering, and observes that as the authors add more attributes, the clustering performance increases overall.
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