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Research and Implementation of Distrubuted Face Detection and Recognition System in Mobile Computing Environment

Author: LiuYaoXing
Tutor: HeXiaoJian
School: South China University of Technology
Course: Applied Computer Technology
Keywords: Mobile computing environment Distributed Systems Face Detection Face Recognition Support Vector Machine Principal Component Analysis
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 82
Quote: 0
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Abstract


Face recognition is a biometric identification technology, is also an important research field of pattern recognition direction, broad application prospects. With the popularity of the smartphone enhance the performance and use, the development of mobile communication technology, mobile Internet within a few years the rapid development and growing faster than almost everyone predicted, and some time in the future will maintain a sustained The development of the state. However, most of the face detection and recognition technology research are applied to the PC platform, or simply to be used alone or in embedded devices, mobile Internet-based smartphones - server distributed interactive mode with Face Detection recognition technology rarely. With mobile intelligent terminal growing popularity and the rapid development of mobile Internet, this distributed face detection and recognition systems in the mobile computing environment has important theoretical and practical significance. Face detection and recognition of this article on the current mainstream algorithm for face detection and recognition were reviewed on the basis of analysis of the characteristics of the mobile computing environment, a mobile computing environment used in smart phones - server distributed architecture the solution of the system and its implementation process. Another analysis of the characteristics of this system architecture. First of all, focuses on the the AdaBoost algorithm principle and process of training a class-based Haar features, as well as the construction process of the cascade classifier cascade classifiers for face detection, and then training on the PC, and to take a variety of optimization method cascade classifier and face detection algorithm is ported to the most popular Android mobile operating system platform, a face detection software based on the Android mobile phone platform, to achieve the purpose of real-time face detection. Finally, the detected face image on the phone through the GPRS network after the split is sent to the server is located on the PC, the next step processing and recognition. Secondly, the theories and methods of statistical learning theory and evolved based on support vector machine. Then on the PC platform, image preprocessing methods including graying, image cropping, geometric normalized normalized and grayscale normalization methods such as face image preprocessing using principal component analysis of the face image. feature extraction, one-to-many strategy training multi-class support vector machine, support vector machine classifier trained to identify classification features obtained classification results. The experiments on the standard library to obtain a good recognition results. In this paper, the design and implementation of mobile computing environment based distributed human face detection and recognition system can be used for video surveillance, real-time criminals identification, authentication, and other security field, and can also be used in the field of mobile games and entertainment, In addition, if the system combined with social networking sites, construct a face recognition-based social networking site, will have a strong appeal and a new user experience.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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