With the rapid development of information technology, face recognition technology has been widely used in video monitoring field. In the field of face recognition application, the accuracy of face detection, as the first step during the face recognition application has a great impact on the performance of face recognition. The face detection needs to be strong robustness, because in practical applications, face images are often affected by a variety of factors, such as illumination, occlusion of light, attitude change, etc. The face detection is the most frequently invoked during face recognition process, so the face detection needs to be able to be executed efficiently. Face detection technology can be realized mainly based on the characteristics of manual design, such as Haar characteristics, LBP (Local Binary Patterns) histogram characteristics, HOG (histogram of oriented gradient) characteristics, etc. The computing time of the characteristics can be accepted, and satisfactory results can be obtained in practical applications, so the characteristics above-mentioned are widely used. However, in the existing technology, it is unable to determine whether the faces in a group of images are from the same person.