Frontal view recognition in multiview video sequences
In this paper, a novel method is proposed as a solution to the problem of frontal view recognition from multiview image sequences. Our aim is to correctly identify the view that corresponds to the camera placed in front of a person, or the camera whose view is closer to a frontal one. By doing so, frontal face images of the person can be acquired, in order to be used in face or facial expression recognition techniques that require frontal faces to achieve a satisfactory result. The proposed method firstly employs the Discriminant Non-Negative Matrix Factorization (DNMF) algorithm on the input images acquired from every camera. The output of the algorithm is then used as an input to a Support Vector Machines (SVMs) system that classifies the head poses acquired from the cameras to two classes that correspond to the frontal or non frontal pose. Experiments conducted on the IDIAP database demonstrate that the proposed method achieves an accuracy of 98.6% in frontal view recognition.