Proceedings of the 1st international convention on Rehabilitation engineering & assistive technology: in conjunction with 1st Tan Tock Seng Hospital Neurorehabilitation Meeting
Proceedings of the 2008 symposium on Eye tracking research & applications
Bio security using face recognition for industrial use
AIC'06 Proceedings of the 6th WSEAS International Conference on Applied Informatics and Communications
Want to save energy?: put intelligence into systems
CSECS'09 Proceedings of the 8th WSEAS International Conference on Circuits, systems, electronics, control & signal processing
The use of eye tracking for PC energy management
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Robust real time face tracking for the analysis of human behaviour
MLMI'07 Proceedings of the 4th international conference on Machine learning for multimodal interaction
Model free head pose estimation using stereovision
Pattern Recognition
A hardware implementation of the user-centric display energy management
PATMOS'09 Proceedings of the 19th international conference on Integrated Circuit and System Design: power and Timing Modeling, Optimization and Simulation
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In this paper, we propose a method for detecting and tracking faces in video sequences in real time. It can be applied to a wide range of face scales. Our basic strategy for detection is fast extraction of face candidates with a Six-Segmented Rectangular (SSR) filter and face verification by a support vector machine. A motion cue is used in a simple way to avoid picking up false candidates in the background. In face tracking, the patterns of between-the-eyes are tracked while updating the matching template. To cope with various scales of faces, we use a series of approximately 1/√2 scale-down images, and an appropriate scale is selected according to the distance between the eyes. We tested our algorithm on 7146 video frames of a news broadcast featuring sign language at 320 × 240 frame size, in which one or two persons appeared. Although gesturing hands often hid faces and interrupted tracking, 89% of faces were correctly tracked. We implemented the system on a PC with a Xeon 2.2-GHz CPU, running at 15 frames/second without any special hardware.