Synchronous Binocular Visible Light Parallax Map for Face Detection

In the binocular vision system, the parallax image is different when the real face of the three-dimensional structure is viewed from different viewing angles, while the face in photos and videos is flat or the planar structure is the same when viewed from different angles, so it is proposed A new method that uses a manual face binocular disparity map made by a synchronized binocular visible light camera combined with a convolutional neural network (convolutional neural network, CNN) to achieve two classifications. First, the Harr cascade classifier is used to extract faces in binocular images at the same time, and the extracted partial faces are made into a data set; then, the disparity map form that meets the experimental hypothesis is proposed and analyzed; finally, the ResNet18 network is used to compare different methods Binocular live body detection solution for human face. The accuracy rate of 99.12% is achieved on the self-made data set in the laboratory, and it has a good resistance to facial attacks on photos and videos. The results show that the method has a small amount of data, low hardware cost and good robustness to human faces due to changes in light and angle.