视频中多线索的人脸特征检测与跟踪1 - æ¸ åŽå¤§å­¦

Brazilian Symposium on Computer Graphics and Image Processing, Gramado, Brazil, 2000.
Multi-Clue Based Facial Feature Detection and Tracking in Video
Li ZHUANG Guangyou XU Haizhou AI Luhong LIANG Zhenyun PENG
Computer Science and Technology Department, Tsinghua University
State Key Laboratory of Intelligent Technology and Systems
Abstract
Video based face recognition has recently attracts much attentions in computer vision and pattern
recognition society. One of the key problems to the success of those video based approaches is the facial
feature detection and tracking. It has been long to realized that it is very difficult to develop a robust
algorithm for facial feature detection and tracking in video due to mainly two factors, lighting and pose
variances. Traditional facial feature detection algorithms have several problems, such as poor adaptability
to environment changes, lacking of self-verification ability, etc. In this paper, we propose a multi-clue
based facial feature detection and tracking method to deal with these problems. Multi-clues include rough
face region segmentation based on disparity or color information, face detection based on multiple related
templates matching, feature detection based on multi-scale Sobel convolution, eye feature verification
based on eigen-eyes, and facial feature verification with both geometry and rigid plane motion constrains
in multiple views. First, binocular stereo video input is used for robustly extracting head region from
complex background through disparity clustering. Then, the multiple related template matching method is
applied to find the accurate face region from this rough segmentation. Facial organ candidates are extracted
from the detected face region at a specific scale space called organ scale for Sobel filter. Eye pair is chosen
from candidates by eigen-eyes method. Finally, nose and mouth corners are detected according to
projections. The algorithm can automatically switch between facial feature detection and tracking based on
embedded verification procedure. In this method multiple clues are joined together to supplement each
other, which makes it possible of the automatically error-checking and even error-correcting, that greatly
improves the algorithm’s adaptability to lighting and face pose changes under complex background.
Experiment results over 189 video sequences demonstrate its effectiveness and robustness.
Keywords: Face recognition, facial feature detection and tracking, face detection, multi-scale analysis
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