Video Face Recognition: A Physiological and Behavioural Multimodal Approach

In this article we present a multimodal system to person recognition by integrating two complementary approaches that work with video data. The first module exploits the behavioural information: it is based on statistical features computed using the displacement signals of a head; the second one is dealing with the physiological information: it is a probabilistic extension of the classic Eigenface approach. For a consistent fusion, both systems share the same probabilistic classification framework: a Gaussian mixture model (GMM) approximation and a Bayesian classifier. We assess the performances of the multimodal system by implementing two fusion strategies and we analyse their evolution in presence of artificial noise.