The Study And Implementation Of Face Recognition System

Face recognition is one of the most challenging problems in the fields of pattern recognition, image processing, and computer vision and cognizance science. It has turned into an active research topic and developed quickly in the recent decades along with various applications. But, to set up practical automatic face recognition system, there are still many problems unsettled, especially the efficiency and robustness of the algorithms.There are many face recognition methods with different traits and application fields. Researchers want to use a testing platform to understand the various algorithms, compare them, develop them and even find or construct new algorithms. Developers hope to use a testing platform to choose a suitable face recognition algorithm in their own products.The paper has the following three main contributions:Ⅰ. An integrated face algorithms testing system is implemented in Visual C++6.0 with OpenCV3.1 development environment with the following traits:A. It integrates 2 face detection algorithms and 3 face recognition algorithms and provides open interface for us to add new algorithms.B. The face information database has promotion value for its document architecture and usability.C. Based on the experiment system, a scheme using the digital watermark to protect face information database is proposed, which can increase the security and enrich the content of face images.Ⅱ. Based on the experiment system, a network face recognition system is implemented. It provides a product prototype for further use. And its network transmission module only transmits the usable face information, which saves a lot of network bandwidth compared with the traditional compressed video method.Ⅲ. Based on the network face recognition system, a series-wound multi-model parallel computing architecture with high efficiency and robustness is proposed. In ideal situation, its recognition rate can reach 100% and its efficiency is immune to the scale of the face information database because of its good expansibility.