A Discriminative Face Geometric Feature-Based Face Recognition

Numerous feature descriptors have been proposed for holistic facial analysis with satisfactory performances. However, few studies have been conducted on how to model the concept of facial beauty for face-related tasks. In addition, existing learning-based face recognition methods suffer from efficiency problems and rely heavily on a sufficient database. In this paper, we develop an efficient method which leverages the perception of human facial beauty for face recognition. Our work is notably different from previous face recognition works in several aspects: (1) we derive a set of facial features based on the fruits of facial attractiveness and beauty analysis research; (2) compared with traditional optimization-based learning methods, we propose simple yet effective C4.5 decision tree as classification model; (3) proposed method is invariant to both gesture variations and facial occlusions, which achieves good performance compared with a series of state-of-the-art methods. More importantly, we propose visualization experiments to verify the discriminative attributes of extracted features. In this paper, we start by preprocessing images in order to enhance image quality. Then, inspired by the facial beauty research, we design a set of new feature descriptors defined as natural feature. The extracted natural feature is embedded into C4.5 decision tree for supervised learning and classification. Extensive experiments are conducted on the AR database, FRGC database and Extended Yale B database. Comprehensive experiments and in-depth analysis verify the effectiveness and competitiveness of the proposed method.