Multi-feature face recognition based on 2D-PCA and SVM

© 2013 Springer Science+Business Media, LLC. All rights reserved. Identification and authentication by face recognition mainly use global face features. However, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with four parts: the left-eye, right-eye, nose and mouth. This method is based on geometrical techniques used to find location of eyes, nose and mouth from the frontal face image. We used 115 face images for learning and testing. Each-individual person's images are divided into three difference images for training and two difference images for testing. The Two-Dimension Principle Component Analysis (2D-PCA) technique is used for feature extraction and the Support Vector Machine (SVM) method is used for face recognition. The results show that the recognition percentage is 97.83%

Multi-feature face recognition based on PSO-SVM

Face recognition is a kind of identification and authentication, which mainly use the global-face feature. Nevertheless, the recognition accuracy rate is still not high enough. This research aims to develop a method to increase the efficiency of recognition using global-face feature and local-face feature with 4 parts: the left-eye, right-eye, nose and mouth. We used 115 face images from BioID face dataset for learning and testing. Each-individual person's images are divided into 3 different images for training and 2 different images for testing. The processed histogram based (PHB), principal component analysis (PCA) and two-dimension principal component analysis (2D-PCA) techniques are used for feature extraction. In the recognition process, we used the support vector machine (SVM) for classification combined with particle swarm optimization (PSO) to select the parameters G and C automatically (PSO-SVM). The results show that the proposed method could increase the recognition accuracy rate. © 2012 IEEE