Research On Embedded Automatic Face Recognition Base On ARM Architecture

Embedded human face recognition is built on the embedded operating system and embedded hardware platform,which involves embedded hardware design,embedded operating system application development,human face recognition algorithms and so on.It is a high starting point,the new concept,practicality AFR.As one of easy to carry,quick installation and mobile AFR,it can be widely applied to different kinds of occasions such as access control systems,outdoor mobile real-time monitoring and other special occasions,so research on embedded face recognition has a strong theoretical significance and wide application.As one of the main research target innovative research projects of Shanghai Municipal Economic Commission-"Radio Frequency Identification RFID system-automatically identify and record the identity of the crowd" (No.04-11-2)and AM Fund project of Shanghai Science and Technology Commission-"Research on Self-Organization Safety Surveillance System Based on ARM and RFID" under the project grant number 0512,starting from the key issues which need to be solved in embedded AFR systems,this study plays emphasis on the real-time face detection,the face key features location,the highly effective person face representation,the robust human face recognition classifiers and AFR system design and so onAutomatic human face detection under the Complex background is the first key issues need to be resolve in the AFR systems,through study the human face detection algorithm base on human skin color model and Haar-like rectangle feature cascade strong classifiers,we found that the face detection algorithm base on human skin color model only use the skin color information without considering the gradation value,and Haar-like rectangle feature cascsde strong classifiers on the country,it only use the gradation value without considering the human skin color information.Thus they have poor robustness to those color-like and face structure-like object under complex background.In view of this,we propose a real-time face detection algorithm based on skin color model verification and the Haar-like features cascade strong classifier in the second chapter.The results show that the algorithm not only solve the color-like and face structure-like object problems under complex background,but also has high detection rate and faster detection rate,and it is robust to light,scales changes under complex background.The human eye detection and pupil location play a very important role on human face normalization and effective human face feature extraction. In order to rapid detect the human eye and precise positioning the human eye pupil center;we propose human eye detection algorithms base on Haar-like features RSVM cascade classifier,and the pupil location algorithms base on the mask and elliptical fitting,the experimental results show that it only take a few hundred milliseconds to complete the human eye detection and pupil centre location the whole process by use the new algorithms,it has fast detection rate and high location accuracyThe same sample size problem in human face recognition will degrade spread matrix in linear discriminant analysis algorithms,and will lead to the problem can not be solved.To solve this problem,we proposes the adaptive linear discriminant analysis algorithm through adjusting the Fisher criterion and making the Improvement to the Fredman thought,Using the complement space of between-class scatter Matrix the algorithm avoids the inverse operation of within-class scatter matrix and adaptively changes the parameter according to the sample information of each class. The experimental result shows that the adaptive linear discriminant analysis algorithm can resolve the SSS problem of FR effectivelyGabor wavelet is robust to image light,scale changes,and it is a good facial feature characterization.But the dimension of excessive Gabor characteristics will lead to dimension disaster of the application system, in order to solve this problem,we proposes Gabor feature extraction algorithms base effective human face region.This algorithms not only reduces the human face feature vector dimension effectively,make small the human face library scale,while reducing the core algorithms of time and space Complexity.And it has same robustness with the traditional Gabor feature extraction algorithm.The support vector machine(SVM),anewmethod for data mining in recent years,has its unique superiority in many fields,such as pattern recognition and nonlinear programming and so on.In this paper,we study multiple classifier strategy and training method of the SVM,and combining with Gabor feature extraction algorithms,adaptive linear discriminant analysis algorithms,develop a strong robustness embedded automatic face recognition system base Windows CE operating systems in ARM platform.To resolve the difficult problem of embedded automatic face recognition and identify in massive face library,we propose a preliminary remote Face Recognition programme which based on client/server architecture wireless network modelThe human face detection,pre-process,normalization and the face feature extraction is completed in the client terminal(embedded hardware platform),and then the client transmits the human face feature data to the server through wireless network.After completing the face recognition and identify in massive face library,the server transmit the result through the wireless network to the client for display.The final performance of embedded FR application system is decided in the very great degree on the highly effective face representation and the robust FR core-algorithm,but the system design strategies are also worthy to be paid attention to.In the Chapterâ…¥,this paper narrated the embedded AFR system design mentality in detail,and discusses the key technical issues in embedded AFR system design with two FR prototype systems demonstrated from the viewpoint of system design.Finally,based on our proposed embedded FR core-algorithms,we realized two embedded FR systems,embedded Intelligent Video Surveillance system and facial image matching system.The systems show good performances in demonstrations and validations.The proposed algorithms can not only contribute to AFR theory,but also have reference values for embedded AFR application system design.