Automatic Gabor Features Extraction for Face Recognition using Neural Networks

In this paper we present a biometric system of face detection and recognition in color images. The face detection technique is based on skin color information. A new algorithm is proposed in order to detect automatically face features (eyes, mouth and nose) and extract their correspondent geometrical points. These fiducial points are described by sets of wavelet components called "jets" which are used for recognition. To achieve the face recognition, we propose two architectures of neural networks and we compare their performances. We also, compare the two types of features used for recognition: geometric distances and Gabor coefficients which can be used either independently or jointly. This comparison shows that Gabor coefficients are more powerful than geometric distances. We show with experimental results how the importance recognition ratio makes our system an effective tool for automatic face detection and recognition.