Combining two visual cortex models for robust face recognition

This paper describes a new robust face recognition system based on combining C2 features of HMAX model and visual attention model inspired from the behavior of neurons in ventral and dorsal stream of visual cortex. By visual attention model, visual attention points are selected which are discriminative areas of face images. C2 features are extracted from these visual attention points to create efficient C2 features and have robustness properties. These efficient C2 features are used to distinct multi-classes of faces. For experimental analysis, we use cropped Caltech faces database and do face recognition task by support vector machine (SVM) classifier on efficient C2 features of them. The results illustrate our proposed face recognition system using efficient C2 features are effective and can recognize faces in various illumination and expression conditions with high accuracy and speed in comparison with original C2 features.