We have witnessed the huge evolution of face recognition technology from the first pioneering works to the current state-of-the-art highly accurate systems in the past few decades. The ability to resist spoofing attacks has not been addressed until recently. While a number of researchers has thrown themselves into the challenging mission of developing effective liveness detection methods against this kind of threat, the existing algorithms are usually affected by limitations such as light conditions, response speed and interactivity. In this paper, a novel and appealing approach is introduced based on the joint analysis of visible image and near-infrared image of faces, three different features (bright pupil, HOG in nose area, reflectance ratio) are extracted to form the final BPNGR feature vector. A SVM classifier with RBF kernel is trained to distinguish between genuine (live) and spoof faces. Experiment results on the self-collected database with 605 samples clearly demonstrate the superiority of our method over previous systems in terms of speed and accuracy.