In this article we present a novel multimodal gender recognition system, which successfully integrates the head and mouth motion information with facial appearance by taking advantage of a unified probabilistic framework. In fact, we develop a temporal subsystem that has an extended feature space consisting of parameters related to head and mouth motion; at the same time, we introduce a complementary spatial subsystem based on a probabilistic extension of the eigenface approach. In the end, we implement an integration step to combine the similarity scores of the two parallel subsystems, using a suitable opinion fusion (or score fusion) strategy. The experiments show that not only facial appearance but also head and mouth motion possess a potentially relevant discriminatory power, and that the integration of different sources of biometric information from video sequences is the key strategy to develop more accurate and reliable recognition systems. I. INTRODUCTIONHuman face contains a variety of information for adaptive social interactions amongst people. In fact, individuals are able to process a face in a variety of ways to categorize it by its identity, along with a number of other demographic characteristics, such as gender, ethnicity, and age. In particular, recognizing human gender is important since people respond differently according to gender. In addition, a successful gender classification approach can boost the performance of many other applications, including person recognition and smart human-computer interfaces.In this article, we address the problem of automatic gender recognition by exploiting the physiological and behavioural aspects of the face at the same time. We have already investigated the use of the head and mouth motion information for person recognition in an earlier research study [1]. Currently, comforted by the promising results obtained by this previous approach, we explore the possibility of using head motion, mouth motion and facial appearance in a gender recognition scenario. Hence, we propose a multimodal recognition approach that integrates the temporal and spatial information of the face through a probabilistic framework.The remainder of this article is organised as follows: in section II we propose a short review of related works, and then in section III we detail our recognition system; afterwards we report and comment the experiments in section IV, and finally we conclude this paper with remarks and future work in section V.

The ability to verify automatically and with great accuracy the identity of a person has become crucial in everyday life. Biometrics is an emerging topic in the field of signal processing. Our research on biometrics aims at developing a complete framework useful to control access. This technical demo shows the latest image processing techniques for face detection developed at France Telecom and for face recognition developed at Eurécom. Using only one computer and one standard webcam, our biometric system detects the user face and the recognition algorithm uses this image to enable the access to a resource, a service or a location.

No abstract