Use of depth and colour eigenfaces for face recognition
Pattern Recognition Letters
Color Face Recognition by Hypercomplex Gabor Analysis
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
An adaptive classification system for video-based face recognition
Information Sciences: an International Journal
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This work aims at frontal face recognition from video. We propose a new Image-to-Image based recognition approach which is both fast and accurate. We use color information for face recognition. Our feature extraction scheme is robust to changes in absolute color values because it uses curvelet transform coefficients to provide edge based representation. This representation makes our scheme invariant to changes in illumination or tanning. Classification is performed by Kernel classifiers such as the Support Vector Machines and a newly proposed Random classifier. As a result, our scheme eliminates the time consuming dimensionality-reduction step (widely used in face recognition), since it is independent of the dimensionality of the input features. Moreover, our parallel architecture allows for computational benefits as well as the ability to integrate depth information in the future. A very short sequence of video (2 seconds) is required for face authentication. Performance evaluations using a standard frontal-face video database show a recognition accuracy of around 99.9%. For short frontal-face video sequences, the proposed scheme outperforms current video based recognition systems by 20%.