An innovative hybrid approach to construct fuzzy-neural network for 3D face recognition system

The 2D face recognition systems encounter difficulties in recognizing faces with illumination variations. The depth map of the 3D face data has the potential to handle the variation in illumination of face images. For feature matching an efficient fuzzy-neural technique is proposed. This paper presents a new approach in which the depth maps of the 3D face images, containing the depth information of the face image are used. Since the input images contain the depth information the input to the fuzzy neural network is illumination invariant. Using the normalized depth map and fuzzy-neural network, a fully automatic 3D face recognition system is developed. The system is evaluated on the 3D face databases; the CASIA database. The proposed system efficiently handles the varying lighting effects and provides significant recognition accuracy.