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Design and Implementation of Face Recognition Based on Hidden Markov Model

Author: CaoBin
Tutor: JinZuoDong
School: Southwest Jiaotong University
Course: Detection Technology and Automation
Keywords: Face Recognition Haar features Cascade classifier Discrete Cosine Transform Hidden Markov Models
CLC: TP391.41
Type: Master's thesis
Year: 2010
Downloads: 160
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Abstract


Face detection and recognition technology as one of the more and more attention in recent years , computer vision research topic has been widely applied to the field of security . Face detection and identification is to use the face of inherent human biometric personal identification process , has a good safety , reliability and validity . This article is mainly for face recognition process in image processing , face detection and recognition technology . First analysis of image pre-processing method used : grayscale , image scaling and image denoising , the focus in the image impulse noise detection and removal method . , By way of example, the analysis showed that the conventional image impulse noise detection method , based on the inaccurate pixels within the detection window most value determination noise standard , may result in a false acceptance noise , increase the image processing time . Simple improvements through the introduction of the distance the average traditional impulse noise detection method . Frequent face detection and real-time requirements , this paper the Hal characteristics (Haar feature) classifier Hidden Markov models (Hidden Markov Model, HMM) combined with face detection and recognition solutions . Classifier based on Haar features face detection , to determine the position of the face . Use characteristics classifiers detection speed , good real-time to make up for the hidden Markov model to determine accurate but slower study based on hidden Markov models to recognize faces in face recognition used the discrete cosine transform of the image feature extraction method . Directly using the image discrete cosine characteristics , the characteristic data is large, cause Face slow . Through the analysis of wavelet transform method , facial feature extraction process , the introduction of the approximation image links based on wavelet image compression to reduce the amount of data to obtain the original image . Then obtain the discrete cosine approximation image characteristics , thereby reducing the computation time of the hidden Markov model to ensure that the method used by the real-time identification system last combined face detection and recognition systems , PC-based platform . Test environment in the face database itself collected , the system achieved better test results .

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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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