A Fast Recognition Method for Pose and Illumination Variant Faces on Video Sequences

Video face recognition is a widely used method in which security is essential that recognizes the human faces from subjected videos. Unlike traditional methods, recent recognition methods consider practical constraints such as pose and illumination variations on the facial images. Our previous work also considers such constraints in which face recognition was performed on videos that were highly subjected pose and illumination variations. The method asserted good performance however; it suffers due to high computational cost. This work overcomes such drawback by proposing a simple face recognition technique in which a cost efficient Active Appearance Model (AAM) and lazy classification are deployed. The deployed AAM avoids nonlinear programming, which is the cornerstone for increased computational cost. Experimental results prove that the proposed method is better than the conventional technique in terms of recognition measures and computational cost.