Improving the Recognition Percentage of the Identity Check System by Applying the SVM Method on the Face Image Using Special Faces

Abstract

Face recognition has attracted tremendous attention during the last three decades because it is considered a simple pattern recognition and image analysis method. Also, many facial recognition patterns have been introduced and used over the years. The SVM algorithm has been one of the successful models in this field. In this article, we have introduced the special faces first. In the following, we have fully explained the SVM method and its subsets, including linear and non-linear support vector machines. Suggestions for improving the recognition percentage of a person's identity check system by applying the SVM method on the face image using special faces are presented. For this test, 10 face images of 40 people (400 face images in total) have been selected from the ORL database. In this way, by choosing the optimal parameter C, determining the most suitable training samples, comparing more accurately with training images and using the distance with the closest training sample instead of the average distance, the proposed method has been implemented and tested on the famous ORL database. The obtained results are FAR=0.23% and FRR=0.48%, which shows the very high accuracy of the operation following the application of the above suggestions.