Abstract

In recent years there has been a growing concern by researchers in developing algorithm for face recognition. The proposed work addresses the problem of face recognition in still images using Extended Kalman Filter for machine learning. The algorithm comprises of designing a feature vector which has discrete wavelet coefficients of the face and, a coefficient representing parameters of the face. Global features of the face are captured by wavelet coefficients and the local feature of the face is captured by facial parameter. The coefficients of the feature vector are used as inputs to the recurrent neural network using EKF algorithm for training. . The proposed algorithm has been tested on various real images and its performance is found to be quite satisfactory when compared with the performance of conventional methods of face recognition such as the Eigen-face method.

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