Recognizing Face Image Based on Gabor and DCT Feature Extraction using SVM

Abstract

- This paper has proposed a hybrid approach by combining the Gabor filter and a Discrete Cosine Transform. Face recognition systems can use in authentication, human-computer interaction, surveillance, and lots of applications. These application users have demanded that a higher accuracy, more efficient, low-cost, and low-calculation time for the facial recognition system. The research issue in image recognition is to maximize recognition accuracy by improving the pre-processing of face-set images, developing the method of extraction of faces, as well as using the most effective face classifier. Feature extraction is an important step that can influence the accuracy of the recognition system. The advantage of the Gabor filter is that it may calculate a large number of features by projection in various directions and sizes. The problem with the Gabor filter is that it has a high dimension and high redundancy and that can be minimized by certain filtering and sampling techniques. In the proposed process, the Gabor features have been filtered by the sampling filtration and the Discrete Cosine Transform extracts the low-frequency features of the Gabor filter sampled. Then assign the obtained optimum features to the support vector machine (SVM) classifier. This algorithm aims to improve accuracy with fewer features for different expressions. The ORL (Olivetti Research Lab) face dataset has used for the experiment. The accuracy of the facial recognition method is 96 percent (verified by using 5-fold cross-validation).