RBS: A ROBUST BIMODAL SYSTEM FOR FACE RECOGNITION

Abstract

During the last few years, many algorithms have been proposed in particular for face recognition using classical 2-D images. However, it is necessary to deal with occlusions when the subject is wearing sunglasses, scarves and such. In the same way, ear recognition is arising as a new promising biometric for people recognition, even if the related literature appears to be somewhat underdeveloped. In this paper, several hybrid face/ear recognition systems are investigated. The system is based on IFS (Iterated Function Systems) theory that are applied on both face and ear resulting in a bimodal architecture. One advantage is that the information used for the indexing and recognition task of face/ear can be made local, and this makes the method more robust to possible occlusions. The distribution of similarities in the input images is exploited as a signature for the identity of the subject. The amount of information provided by each component of the face and the ear image has been assessed, first independently and then jointly. At last, results underline that the system significantly outperforms the existing approaches in the state of the art.