Video-Based Face Recognition Using A Probabilistic Graphical Model

[[abstract]]We present a probabilistic graphical model to formulate and deal with video-based face recognition. Our formulation divides the problem into two parts: one for likelihood measure and the other for transition measure. The likelihood measure can be regarded as a traditional task of face recognition within a single image, i.e., to estimate how similar to a specified person this observing face image is. In our work, two-dimensional linear discriminant analysis (2DLDA) is employed for feature extraction, and then we use a Gaussian distribution to assess the likelihood measure. The transition measure is estimated via two terms, person transition and pose transition. The transition terms could fix some incorrect recognition results because of considering the information between adjacent frames. In the face recognition experiments, we adopt two datasets, Honda/UCSD dataset and VIPlab dataset. Finally, it is demonstrated that our proposed approach is robust in different datasets and produces good recognition accuracy which is more than 90%.