Face Tracking and Recognition with Visual Constraints in Real-World Videos
Venue
IEEE Computer Vision and Pattern Recognition (CVPR) (2008)
Publication Year
2008
Authors
Minyoung Kim, Sanjiv Kumar, Vladimir Pavlovic, Henry A. Rowley
BibTeX
Abstract
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 recognizer with state-of-the-art recognition performance.
We test the quality of tracking and face recognition on realworld noisy videos from
YouTube as well as the standard Honda/UCSD database. Our approach produces
successful face tracking results on over 80% of all videos without video or
person-specific parameter tuning. The good tracking performance induces similarly
high recognition rates: 100% on Honda/UCSD and over 70% on the YouTube set
containing 35 celebrities in 1500 sequences.
