# A New Face Recognition Method for Intelligent Security
## 6 Conclusions  
Applying face recognition technology to the perception system of security robots can improve the efficiency of face recognition in intelligent security robots. With the help of the Internet of Things and the big data environment, we propose a new method for face recognition for intelligence security in this paper. First, a four-step phase-shifted fringe pattern is generated by a computer and transmitted to a projector. Then, it is projected onto the background surface and the surface of the object to be measured. Second, the camera obtains the background fringe pattern and the deformed fringe pattern separately and transmits them to the computer to solve the wrap phase value with the four-step phase-shift method. Then, the computer obtains the unwrapped phase value through an optimized Goldstein branching method and finds the height with the phase-to-height transformation formula, and thus reconstructs a 3D face image. Next, the system extracts the side contour line and the horizontal contour line and other radial curves from the nose-tip point on the face and performs improved layered matching and point-distance matching on different curves. Finally, it assigns two degrees of matching  
Figure 16: Recognition performances of different algorithms in different attitudes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Recognition Rate** & **Speed/s** \\ \hline ICP & 95.95\% & 4.66 \\ SP & 90.42\% & 3.34 \\ LBP & 89.73\% & 4.89 \\ This Method & 97.10\% & 2.81 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance comparison among different face recognition methods.  
to different weights for a weighted fusion to find the total similarity for the 3D face recognition. This method requires a relatively simple set of equipment which includes only a computer, a projector, and a camera. As the experiments above showed, compared to other algorithms such as ICP, SP, and LBP, this method has a relatively higher recognition rate (97.10%) on human faces with a faster speed of 2.81 s. Calculations show the method is robust to attitude, illumination, and noise and achieves a better recognition performance. It can be applied to smart cities [30] and civil security where intelligent security robots with efficient face recognition functions in the big data environment can be considered as innovations of the traditional security industry and public safety services. In the future, we hope to improve the face recognition rate and the resolution of the facial expressions, enrich the users' scenarios, and increase market acceptance.  
Conceptualization, Z.W. and X.Z.; methodology, Z.W. and X.Z. and P.Y.; software, X.Z.; validation, X.Z. and P.Y.; formal analysis, Z.W.; investigation, W.D.; resources, Z.W. and W.D.; data curation, Z.W.; writing--original draft preparation, Z.W. and X.Z.; writing--review and editing, X.Z., D.Z., and N.C.; supervision, Z.W. and N.C.; project administration, Z.W. and N.C.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Science and Technology Support Plan Project of Hebei Province, grant number 17210803D and 19273703D; The Science and Technology Spark Project of Hebei Seismological Bureau, grant number DZ20180402056; The Education Department of Hebei Province, grant number QN2018095.  
The authors declare no conflicts of interest.