  We present in this paper a biometric system of face detection and recognition
in color images. The face detection technique is based on skin color
information and fuzzy classification. 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 which are used for recognition. To achieve the face
recognition, we use neural networks and we study its performances for different
inputs. We 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.
