Abstract
Recognition of faces under varied poses has been a challenging area of research due to the complex dispersion of poses in feature space when compared to that of frontal faces. This paper presents a novel and robust pose-invariant face recognition method in order to improvise over existing face recognition techniques. First, we apply the TSL color model for detecting facial region and estimate the direction of face using facial features. The estimated pose vector is decomposed into X-Y-Z axes. Second, the input face is mapped by a deformable template using these vectors and the 3D CANDIDE face model. Finally, the mapped face is transformed to the frontal face which appropriates for face recognition by the estimated pose vector. Through the experiments, we come to validate the application of face detection model and the method for estimating facial poses. Moreover, the tests show that recognition rate is greatly boosted through the normalization of the poses.
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