Fusion of deep and shallow features for face recognition

Abstract

The traditional method for feature extraction contains limited discriminant features, and the deep learning method need lots of labeled data and it′s time-consuming. This paper presents a method which fuses the deep and shallow features for face recognition. Firstly, the HOG feature is extracted from each images and the dimensionality reduction is followed; and the PCANet feature is extracted simultaneously and its′ dimension is reduced. Secondly, the fusion of the two types of features is conducted and discriminant features are extracted further. Finally, the SVM is adopted for classification. Experiments on the AR database verify the effectiveness and robustness of the proposed method.

Keywords

• face recognition
• feature fusion
• shallow features
• deep features
• hog