Face recognition is widely applied in various practical conditions such as entrance guard due to its convenience and practicability. But it is vulnerable to various forms of spoofing attacks (such as photo attacks and video attacks). The liveness detection based on deep Convolution Neural Network (CNN) can solve the above problem but has disadvantages such as high calculation cost, unfriendly interaction mode and difficult deployment on embedded devices. Therefore, a real-time and lightweight security classification method of face recognition was proposed. The face liveness detection algorithm based on color and texture analysis was integrated with the face authentication algorithm, and a face recognition algorithm performing face liveness detection and face authentication in the situation of monocular camera without user cooperation was proposed. The proposed algorithm can support real-time face recognition and has higher liveness recognition rate and robustness. In order to validate the performance of the proposed algorithm, Chinese Academy of Sciences Institute of Automation-Face Anti-Spoofing Dataset (CASIA-FASD) and Replay-Attack dataset were utilized as the benchmark datasets of the experiment. The experimental results show that, in the liveness detection, the proposed algorithm has the Half Total Error Rate (HTER) of 9.7% and Equal Error Rate (EER) of 5.5% respectively, and has the time cost of 0.12 s to process a frame of image in the whole process. The above results verify the feasibility and effectiveness of the proposed algorithm.