On Channel Reliability Measure Training for Multi-Camera Face Recognition

Single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. Face recognition using multiple synchronized cameras is proposed to overcome the limitations. We introduce a reliability measure trained from examples to evaluate the inherent quality of channel recognition. The recognition from the channel predicted to be the most reliable is selected as the final recognition results. In this paper, we enhance Adaboost to improve the component based face detector running in each channel as well as the channel reliability measure training. Effective features are designed to train the channel reliability measure using data from both face detection and recognition. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules