Weighted voting component-based face recognition

Abstract

The face Recognition researched within this paper compares the component based on the block and global based recognition methods. The method presented in this paper consists of the following major steps: 1- Facial component detection and cropping using one of the following: a- The Viola-Jones Object Detection Framework b- Facial Landmarks 2 - Component Representation Block and component-based face recognition methods rely on the histogram of oriented Gradients (HOG features) to represent the facial components, while the global-based face recognition uses the distance vector to represent the face. 3 - Recognition Model Training Block and component-based face recognition methods train a separate model for each facial component. The global based face recognition method trains one model for each person. KNN and SVM machine learning methods have been used and compared in each case. Three public databases were used: 1 - AT&T with 40 subjects and 400 images. 2 - PUT Database with 50 subjects and 1100 images. 3 - AR database with 50 subjects and 1300 images. The Improvement observed using the method presented in this paper are as such: 1- nearly 100% accurate in detecting facial components during all pose changing circumstances. 2- Improve the machine learning classification accuracy by combining multiple Classifications Through the method of majority voting, allowing the ability to overcome the illumination and pose change at the same time.