Pose and illumination invariant face recognition using binocular stereo 3D reconstruction

Traditional 2D face recognition systems drastically fails with pose variance and poor illuminations. Many techniques but with limited success has been introduced. Expensive 3D setup can be used to deal with this problem. In this work a low cost, low computation and quick good quality 3D reconstruction helping 2D face recognition systems is proposed. The proposed system is a fast automatic 3D face reconstruction approach from rectified stereo images. An automatic synthesis of training images of various face poses is proposed. Three enhancements adaptive histogram equalization (AHE) to improve contrast of face images, horizontal gradient ordinal relationship pattern(HGORP) to handle poor illumination and steerable filter(SF) for noise reduction and illumination invariance are used to improve the system performance. Later SURF based matching is done with score level fusion of all three enhancements. A database of 107 subjects has been collected to evaluate the system performance. It is observed that the proposed system can handle large pose variations and poor illumination very well.