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. The view variations are handled by using the moment invariants. Moment Invariants are used as rotation invariant features of the face image. For feature matching an efficient fuzzy-neural technique is proposed. The PCA components of normalized depth map and Moment invariants on mesh images are used successfully to implement a fuzzy neural network based fully automatic 3D face recognition system. The system is evaluated on the 3D face databases, the CASIA database. The proposed system provides recognition accuracy that is resulted in to an efficient 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.
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