Aiming at the problem that face recognition systems are suspectable to be affected by forgery attacks, a face liveness detection method based on near-infrared and visible binocular vision was proposed. Firstly, the binocular device was used to obtain the face images of near-infrared and visible light synchronously. Then, the facial feature points of two images were extracted, and the binocular relation was used to match the feature points and obtain their depth information, which was used for three-dimensional point cloud reconstruction. Secondly, all facial feature points were divided into four regions, and the average variance of facial feature points in the depth direction within each region was calculated. Thirdly, the key feature points of face were selected. With the nasal tip point as the reference point, the spatial distances between the nasal tip point and the key feature points were calculated. Finally, the feature vectors were constructed by using the depth value variances and spatial distances of facial feature points. And Support Vector Machine (SVM) was used for the judgment of real faces. The experimental results show that the proposed method can detect real faces accurately and resist the attacks of fake faces effectively, achieves the recognition rate of 99.0% in experimental tests, and is superior in accuracy and robustness to the similar algorithm using depth information of facial feature points for detection.