# A New Face Recognition Method for Intelligent Security
## 1 Introduction  
In recent years, the domestic security industry market has developed rapidly. As intelligence becomes a major trend in the industry, intelligent security has gradually become the direction of the transformation and upgrading of security companies, and its proportion in the security industry will become increasingly larger. In 2019, the market size of China's security industry was approximately 720 billion yuan. It is estimated that in 2020, intelligent security will create a market worth about 100 billion yuan, and intelligent security will be an important market in the security field. There is a wide range of applications of the Internet of Things in smart cities, civil security, and some focusing industries. Among these, biometric technology is one of the most important critical technologies. Face recognition technology research based on big data systems is of great significance. With the technology of big data and artificial intelligence developing rapidly, the big data environment not only provides a good basis for the in-depth development of face recognition systems but also realizes the sharing of feature databases in wider fields, which is helpful for achieving more abundant face feature databases. Therefore, face recognition and artificial intelligence are connected and interact safely and seamlessly with big data systems, which have become an important technical support in social security systems. Intelligent security robots with a face recognition function are being developed. This kind of intelligent security robot will be used in many public places to accurately scan human facial features, make judgments, and record personal data and behaviors in the cloud and feed back to the background. It issuperior to the traditional surveillance camera and has great practical significance for the development of the security industry. The current biometric technology [1] includes face recognition, fingerprint recognition, palm print recognition, iris recognition, etc. Face recognition is the focus of researchers in the fields of pattern recognition and computer vision. It has the characteristics of noncontact and non-aware biometrics, compared with other biometric technologies such as fingerprint recognition and iris recognition. Currently, most face recognition devices are mainly based on two-dimensional images [2], but these devices are susceptible to ambient light, background, and shooting angle. In order to overcome this shortcoming, the technologies of geometric information and relative position information, which belong to the technologies of three-dimensional (3D) face images, are widely used because of their relatively more stable nature. Mainstream 3D imaging technologies include stereo vision, structured light [3], time of flight (TOF), and binocular ranging. Therefore, face recognition based on 3D face data achieves a more accurate recognition result. In the context of big data, it can quickly and accurately automatically recognize human faces and apply face recognition technology to the perception system of security robots. We show a new method for face recognition for intelligence security in this paper. To recognize 3D faces, we must first preprocess the reconstructed 3D face images. In the study of image processing, a method of describing a digital image as a vector in terms of a frequency histogram of graphs and mapping the image into a vector space through a graph transformation is presented [4]. There is also a method in which an image is encoded by a region adjacency graph (RAG), based on multicolored neighborhood (MCN) clustering algorithm [5]. Three-dimensional face recognition is divided into two parts: measurement and algorithm. As an important three-dimensional measurement method, grating projection measurement has the advantages of noncontact, low cost, and high precision and is widely used in industrial detection, reverse engineering, biomedicine, virtual reality, and other fields [6; 7]. The projected grating phase method was first proposed by [PERSON] [8] in 1982.