This paper presents an automatic video based face verification and recognition system by Support Vector Machines (SVMs). Faces as training samples are automatically extracted from input video sequences in real-time by LUT-based Adaboost and are normalized both in geometry and in gray level distribution after facial landmark localization via Simple Direct Appearance Model (SDAM). Two different strategies for multi-class face verification and recognition problems with SVMs, "one-vs-all" and "one-vs-another", are discussed and compared in details. Experiment results over 100 clients are reported to demonstrate the effectiveness of SVM on video sequences.
In this paper a new algorithm to locate the Elastic Labeled Graph is proposed for the face recognition approach based on Gabor wavelet jets. We extend Direct Appearance Model (DAM) to a hierarchical organization, which performs faster and more remote compared with the traditional graph localization method used in Elastic Bunch Graph Matching. A tracking recognition scheme is further discussed through employing the hierarchical DAM in a video sequence. Experimental results demonstrate the effectiveness of the method in locating and tracking the elastic graph.
A coarse-to-fine facial feature detection and tracking system which is used under complex background is introduced in this paper. The system uses stereo cameras for video input. By stereovision technique, face is roughly and quickly segmented from complex background. Then, the multiple 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. Finally, eyes, nose and mouth corners are detected. Techniques for checking and correcting errors in facial feature detection based on multiple cues are developed to make the algorithm more robust in facial feature detection and tracking in video sequence. Experiments on 189 video sequences demonstrate its effectiveness.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.