Modified approach for recognition of 3-D faces with missing parts

This paper presents an implementation of face recognition, which is a very important task of identifying human faces. Representation of a face image is dealing with keypoints clustering and curve matching approach. In our work we implement the methods for recognition of a 3-D face image with missing and occluded part. The solutions for this problem is found out with the help of SIFT and RANSAC algorithms and the keypoints measurements and feature matching this are the actual flow of implementation. A partial representation of a face image is totally related to 3-D applications. Features of a frontal images and left/right images will be considered. Surface matching is that form of a recognition that with the help of this just calculating the keypoints. A canonical representation of the face is that which exploits the isometry invariance of the face surface to manage missing data obtained by randomly removing areas from frontal face scans. Both shape and surface reactance properties are calculated locally through minimization of the image differences between the original images and their estimations. Stochastic computational methods and integrability enforcement are employed to handle the nonlinearity and inconsistency issues in the shape and reactance parameter optimization processes to achieve valid results.