Real time 2-D face detection using color ratios and K-mean clustering
Proceedings of the 44th annual Southeast regional conference
An Improved Hybrid Projection Function for Eye Precision Location
Medical Imaging and Informatics
Human-computer interaction system based on nose tracking
HCI'07 Proceedings of the 12th international conference on Human-computer interaction: intelligent multimodal interaction environments
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Facial feature extraction using PCA and wavelet multi-resolution images
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Real-time implementation of face detection for a ubiquitous computing
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and its Applications - Volume Part I
Information Sciences: an International Journal
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A method for face recognition is proposed which uses a two-step approach: first a number of facial components are found, which are then glued together, and the resulting face vector is recognized as representing one of the possible persons. During the extraction step, a wavelet statistics subsystem provides the possible locations of eyes and mouth which are used by the Support Vector Machine (SVM) subsystem to extract facial components. The use of wavelet statistics subsystem speeds up the recognition process markedly. Both the feature detection SVMs and wavelet statistics are trained on a small number of actual images with features marked. Afterwards, a large number of face vectors are constructed, which are then classified with another set of SVM machines.