# A New Face Recognition Method for Intelligent Security
## 1 Introduction
Information on object height is obtained by demodulating the fringes that are previously modulated by the height on the surface of the object in this method. Common fringe projection measurement techniques include phase measuring profilometry (PMP) [9; 10], Fourier transform profilometry (FTP) [11], and Moire profilometry (MP). Compared to Fourier transform profilometry and convolutional demodulation, phase measurement profilometry requires more fringe patterns and slightly longer acquisition time. However, PMP has advantages such as a simpler solving process, fewer computations, and a resistance to the background influence. The method directly finds the phase value in fringe patterns through mathematical calculations, and thus, we use PMP for the wrap phase in fringe patterns. The frequency of phase shifts determines speed and accuracy. Fewer phase shifts lead to a faster speed, but the accuracy is lower. Accordingly, more phase shifts help improve the accuracy it may reach, but the speed is slower. Considering the two factors of speed and accuracy, we use a four-step phase-shift method as the demodulation method of the fringe phase. We use an improved Goldstein branching method for a better quality of phase unwrapping patterns and achieve practical significance for the accuracy of 3D reconstruction in human faces. There are also many researches on algorithms for 3D face recognition. The algorithm introduced by [PERSON] et al. [12] automatically recognizes six basic facial expressions (anger, disgust, fear, joy, sadness, and surprise) by dividing the face image into 79 regions and comparing the feature values obtained in each region with a threshold. In a study by [PERSON] et al. [13], an algorithm is explored for face recognition by using geometric descriptors (such as mean, Gaussian, path, shape index, curvature, and coefficients of basic form) to extract 3D facial features and analyze them. In a study by [PERSON] and colleagues [14], a method which combines 2D and 3D facial features is used for facial expression analysis and recognition. In our daily life, the faces of criminal suspects are often blocked through camouflage, which causes certain difficulties in face recognition. Aiming at this problem, a detection method and repair strategy are proposed by providing a database and corresponding experiment [15]. Generally speaking, these 3D face recognition algorithms are divided into the following three main categories: spatial-based direct matching, overall feature matching, and local feature matching. Spatial-baseddirect matching directly performs similarity matching on surfaces without the extraction of features. Commonly used matching methods include iterative closest point (ICP) [16] and the Hausdorff distance method [17]. It has a good effect on the matching of rigid surfaces, but it is susceptible to expression changes since the human face is a nonrigid surface. Overall feature matching focuses on the overall features of 3D faces. Those methods include the apparent matching based on depth patterns and methods based on the extended Gaussian image (EGI). Local feature matching mainly extracts local features of human faces for matching. [PERSON] and [PERSON] [18] extracted the center contour line through the most prominent point on the curves of human faces and two contour lines parallel to it and obtained similarities among three contour lines by comparing the curvature values on the selected contour lines. Further fusion among them then resulted in the similarity of the human face. However, this method only extracts three curves on human faces, which thus leads to a severe loss of curvature information and a relatively narrower representation. [PERSON] et al. [19] extracted equal-depth contours on the depth pattern of human faces and calculated the geodesic distance between the equal-depth contours of the test face and the library set face on the Riemann space as their matching similarity. [PERSON] et al.