A Real-Time Face Detection Method Based on Blink Detection

Face anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face. In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system using a legitimate user's picture. Aiming at this problem and the influence of illumination in real-time video face recognition, this paper proposes a real-time face detection method based on blink detection. The method first extracts the image texture features through the LBP algorithm, which eliminates the problem of illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from different angles or at different times to obtain more facial details. In addition, the fusion of local and global features is realized by SPP pooling, which enriches the expression ability of feature maps and improves detection accuracy. Finally, the eye EAR value is calculated by the face key point detection technology to achieve face anti-spoofing, and then the real-time face recognition against fraud is realized. The experimental results show that the algorithm proposed in this paper has good accuracy on NUAA, CASIA-SURF and CASIA-FASD datasets, which can reach 99.48%, 98.65% and 99.17%, respectively.