Face Recognition Under Significant Pose Variation

Unlike the frontal face detection, multi-pose face detection and recognition techniques, still face the following challenges: large variability of environments such as pose, illumination and backgrounds and unconstrained capturing of facial images. We introduced a new system to deal with this problem. First, the two-step color-based approach is used to find candidate area of face from original picture. Then rough estimator of five poses is created using AdaBoost technique. In order to accurately locate the candidate face, multiple statistical shape models-ASM (active shape models) are proposed to estimate accurate pose of model of the input image and to extract facial features as well. In recognition step, we use geometrical mapping technique to deal with the pose variation and face identification.