Recognising the Dynamics of Faces across Multiple Views

We present an integrated framework for dynamic face detection and recognition, where head pose is estimated using Support Vector Regression, face detection is performed by Support Vector Classification, and recognition is carried out in a feature space constructed by Linear Discriminant Analysis. Unlike most traditional approaches to matching the patterns from static face images, we model the dynamics of human faces from video sequences in a consistent spatio-temporal context, i.e. recognition is accomplished by matching an object trajectory to a set of identity model trajectories in feature space. The model trajectories are synthesized from only a few views which sparsely cover the view sphere. Compared with the static face matching techniques, this approach is more robust and accurate under a coarse correspondence of face images, and has potential to visual interaction and advanced human behaviour recognition in real-world scenarios.

• Type: Conference
• Year: 2000
• Where: BMVC
• Authors: Yongmin Li, Shaogang Gong, Heather M. Liddell
• Keywords: BMVC 2000, Computer Vision, Face Detection, Face Images, Static Face