Facial Component Extraction and Face Recognition with Support Vector Machines

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.