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The Research of Gait Recognition

Author: JiShuangFeng
Tutor: HuangFengGang
School: Harbin Engineering University
Course: Applied Computer Technology
Keywords: Gait Recognition Motion detection Background subtraction Hidden Markov Models Principal Component Analysis
CLC: TP391.41
Type: Master's thesis
Year: 2008
Downloads: 68
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Abstract


With the continuous development of science and technology , modern society, a higher demand for human self - identification accuracy , safety and practicality , the biometric technology is quietly rising . Gait recognition is the biometric technology emerging sub- field , by people walking posture identification and authentication of personal identity . This article will use the hidden Markov model (HMM, Hidden Markov Model) gait identification and better recognition effect . The paper first describes the current gait recognition research Difficulties and trends , and then generalize the commonly used research methods of gait recognition ; then explain in detail the the theoretical focus HMM of this chapter , including its source , involving the main algorithm to achieve in the main problem ; for three achieve part of the gait recognition system : the image sequence preprocessing, feature extraction and gait recognition , this paper gives corresponding solutions , and the final recognition result . The focus of this study Gait Extraction: outline of the movement of the human body through the pre- image the pretreatment work ( including background modeling , foreground detection and morphological processing ) , after the body contour edge points to its centroid distance and outline stride for gait characteristics ; extracted five key frames in a gait cycle , according to the K-means clustering algorithm to find the Euclidean distance between each frame and keyframe using principal component analysis feature parameters to reduce the dimension ; everyone to establish a continuous HMM, and then identify the sequence of states seeking to be established HMM output probability , take the corresponding output probability category as its class label . The proposed method can be well described in the body of the gait characteristics , the statistical nature of the HMM has good gait recognition soundness , change the speed of movement has strong robustness . The experimental results show that the HMM is an effective method in the field of gait recognition , and has a strong potential for development .

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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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