Face tracking and recognition with visual constraints in real-world videos

We address the problem of tracking and recognizing faces in real-world, noisy videos. We track faces using a tracker that adaptively builds a target model reflecting changes in appearance, typical of a video setting. However, adaptive appearance trackers often suffer from drift, a gradual adaptation of the tracker to non-targets. To alleviate this problem, our tracker introduces visual constraints using a combination of generative and discriminative models in a particle filtering framework. The generative term conforms the particles to the space of generic face poses while the discriminative one ensures rejection of poorly aligned targets. This leads to a tracker that significantly improves robustness against abrupt appearance changes and occlusions, critical for the subsequent recognition phase. Identity of the tracked subject is established by fusing pose-discriminant and person-discriminant features over the duration of a video sequence. This leads to a robust video-based face reco...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley
• Keywords: Abrupt Appearance Changes, Computer Vision, CVPR 2008, Face Recognition, Realworld Noisy Videos, Subsequent Recognition Phase, Video-based Face Recognizer