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Using SIFT Descriptors for Face Recognition Based on Neural Network and Kepenekci Approach

The principal objective of this paper is to develop a face matching method based on facial feature extraction. The first stage to build a robust face matching system is to extract corresponding points between a pair of images. A method based on feature vectors has been used to match images. Since the images illumination, motion, rotation, and scale are different, we have used the SIFT algorithm, which is robust to these variations, for extracting Keypoints. After determining Keypoints for both images and calculating their respective feature vectors, the degree of similarity between two images is evaluated. Besides, the feature vectors of the images are compared with the feature vectors of each reference image to determine the overall similarity between two images. In this paper, we use the SIFT algorithm along with the neural network and the Kepenekci approach and compare the results of these two methods.