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http://dx.doi.org/10.6109/jkiice.2012.16.10.2121

A Study on Eigenspace Face Recognition using Wavelet Transform and HMM  

Lee, Jung-Jae (송원대학교)
Kim, Jong-Min (조선대학교, JMSoft)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.16, no.10, 2012 , pp. 2121-2128 More about this Journal
Abstract
This paper proposed the real time face area detection using Wavelet transform and the strong detection algorithm that satisfies the efficiency of computation and detection performance at the same time was proposed. The detected face image recognizes the face by configuring the low-dimensional face symbol through the principal component analysis. The proposed method is well suited for real-time system construction because it doesn't require a lot of computation compared to the existing geometric feature-based method or appearance-based method and it can maintain high recognition rate using the minimum amount of information. In addition, in order to reduce the wrong recognition or recognition error occurred during face recognition, the input symbol of Hidden Markov Model is used by configuring the feature values projected to the unique space as a certain symbol through clustering algorithm. By doing so, any input face will be recognized as a face model that has the highest probability. As a result of experiment, when comparing the existing method Euclidean and Mahananobis, the proposed method showed superior recognition performance in incorrect matching or matching error.
Keywords
Wavelet transform; Principal Component Analysis; Hidden Markov Models; Eigenspace; Singular Value Decompos;
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