Image Recognition Technology
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Abstract
Presently, face recognition is a popular technology for accessing multimedia content in different networks, such as security, indexing retrieves content or video compression. The facial recognition technology is "people" as the center of attention for accessing various multimedia information. Controlling network access with facial recognition technology will not only protect “passwords” from stealing, but also stimulate interaction between users and computers. Indexing or extracting videos based on a specific person's appearance is helpful for users such as reporters, scientist and moviegoers. Video calling and teleconferencing applications using facial recognition technology also provide a more robust encryption scheme. Regarding image processing, the objective of this paper is to explain a general framework for facial recognition systems and the variations commonly found in facial recognition machines which are several well-known face recognition algorithms such as Eigenfaces and neural networks. Face image recognition procedures are tested with various facial expressions for comparing to each other. The result shows that the error rate of the mixed method Self-Organizing Map and Neural Networks is 4.5 percent, a minimum time of recognizing at 0.7 seconds for 6 hours training time. The error rate of the Eigenface method is 9 percent about 40 minutes for practice time which is the least time for testing. The selected method depends on which method is suitable for their own work. The Neural Network method shows the result better than others method.
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