Face liveness detection with feature discrimination between sharpness and blurriness

Face recognition has been extensively used in a wide variety of security systems for identity authentication for years. However, many security systems are vulnerable to spoofing face attacks (e.g., 2D printed photo, replayed video). Consequently, a number of anti-spoofing approaches have been proposed. In this study, we introduce a new algorithm that addresses the face liveness detection based on the digital focus technique. The proposed algorithm relies on the property of digital focus with various depths of field (DOFs) while shooting. Two features of the blurriness level and the gradient magnitude threshold are computed on the nose and the cheek subimages. The differences of these two features between the nose and the cheek in real face images and spoofing face images are used to facilitate detection. A total of 75 subjects with both real and spoofing face images were used to evaluate the proposed framework. Preliminary experimental results indicated that this new face liveness detection system achieved a high recognition rate of 94.67% and outperformed many state-of-the-art methods. The computation speed of the proposed algorithm was the fastest among the tested methods.