A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos

PATTERN RECOGNITION

卷:64

Article

Wu, Baoyuan^1,2  Hu, Bao-Gang¹  Ji, Qiang³ 

[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China

[2] King Abdullah Univ Sci & Technol, Visual Comp Ctr, Thuwal 239556900, Saudi Arabia

[3] Rensselaer Polytech Inst, Dept Elect Comp & Syst Engn, Troy, NY 12180 USA

关键词: Face clustering;    Face tracking;    Coupled Hidden Markov Random Field;   

DOI  :  10.1016/j.patcog.2016.10.022

来源: Elsevier

【 摘 要 】

Face clustering and face tracking are two areas of active research in automatic facial video processing. They, however, have long been studied separately, despite the inherent link between them. In this paper, we propose to perform simultaneous face clustering and face tracking from real world videos. The motivation for the proposed research is that face clustering and face tracking can provide useful information and constraints to each other, thus can bootstrap and improve the performances of each other. To this end, we introduce a Coupled Hidden Markov Random Field (CHMRF) to simultaneously model face clustering, face tracking, and their interactions. We provide an effective algorithm based on constrained clustering and optimal tracking for the joint optimization of cluster labels and face tracking. We demonstrate significant improvements over state-ofthe-art results in face clustering and tracking on several videos.

【 授权许可】

Free