A study on face recognition in video surveillance system using multi-class Support Vector Machines

In this paper, we present an evaluation of face recognition algorithm in video surveillance system using multi-class Support Vector Machine (SVM). We also show how the variation of sizes (due to distance) and intensity of training images (due to camera hardware) can improve the face recognition rate. The system starts by detecting the face regions using AdaBoost training algorithm. Holistic approach is chosen to extract features from the detected face regions as this approach is the simplest. After the feature vectors are generated, multi-class SVM is used for classification step. SVM is chosen as the classifier as it is a low samples, high dimensional classifier. As the training and test set, we use SCface database in which the images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Based on our study using only linear kernel multi-class SVM, it is shown that distance of approximately 2.6m gives the best results, and the training and test images have to be from similar intensity cameras although surveillance camera will have different quality of images.