Face Recognition Using Vector Quantization Histogram and Support Vector Machine Classifier

Abstract

Abstract. In this paper, we propose a new face recognition method combining Vector Quantization (VQ) method and Support Vector Machine (SVM) classifier. VQ method is used as a feature extractor and SVM classifier for feature classification. By applying low pass filtering and VQ processing to a facial image, a histogram including effective facial feature is generated, which is called VQ histogram. After dividing VQ histograms into training set and testing set, classifiers are trained with training examples (training histograms) by using Gradient Descent Method (GDM). Testing examples (testing histograms) can be tested with optimal classifiers for face recognition. We use the publicly available ORL face database for the evaluation of recognition accuracy, which consist of 400 images of 40 individuals. Experimental results show that the variety of filter size affects the recognition accuracy. The recognition rate increases with an increase of the ratio of training examples and testing examples, and maximum recognition rate of 98.0 % is obtained.

Evaluating the Performance of Eigenface, Fisherface, and Local Binary Pattern Histogram-Based Facial Recognition Methods under Various Weather Conditions

“…Most facial recognition (FR) studies are conducted in indoor conditions [14][15][16][17][18][19]. However, none of these studies considered any sort of outdoor situation.…”

Comparison of CNNs and SVM for voice control wheelchair

“…Support Vector Machine (SVM) can be categorised as one of the best classifications techniques. SVM also has been applied in various applications for instance in biometrics [17,[22][23], sentiment analysis [24][25] and security such as intrusion detection [26][27]. Selvakumari and Radha [28] applied SVM in classifying speech pathology and achieved 98% accuracy compared to the Naïve Bayes algorithm.…”