Gait Recognition Method Based on Wavelet Transformation and its Evaluation with Chinese Academy of Sciences (CASIA) Gait Database as a Human Gait Recognition Dataset
{"title":"Gait Recognition Method Based on Wavelet Transformation and its Evaluation with Chinese Academy of Sciences (CASIA) Gait Database as a Human Gait Recognition Dataset","authors":"K. Arai, Rosa Andrie","doi":"10.1109/ITNG.2012.164","DOIUrl":null,"url":null,"abstract":"Human Gait: HG recognition method based on wavelet transformation is proposed. Using Chinese Academy of Sciences (CASIA), the proposed method is evaluated and is compared to the conventional HG recognition method without utilizing wavelet transformation. In particular, two preprocessing methods, model based and model free methods are attempted for the proposed HG recognition. Also 2D Discrete Wavelet Transform (DWT), and 2D lifting Wavelet Transform (LWT) level 1 decomposition are features in the proposed HG recognition method. Haar base function of wavelet transformation is also used for feature extraction in the proposed method. Experimental results with CASIA database show x % improvement in terms of correct classification performance in comparison to the conventional method.","PeriodicalId":117236,"journal":{"name":"2012 Ninth International Conference on Information Technology - New Generations","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Ninth International Conference on Information Technology - New Generations","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITNG.2012.164","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
Human Gait: HG recognition method based on wavelet transformation is proposed. Using Chinese Academy of Sciences (CASIA), the proposed method is evaluated and is compared to the conventional HG recognition method without utilizing wavelet transformation. In particular, two preprocessing methods, model based and model free methods are attempted for the proposed HG recognition. Also 2D Discrete Wavelet Transform (DWT), and 2D lifting Wavelet Transform (LWT) level 1 decomposition are features in the proposed HG recognition method. Haar base function of wavelet transformation is also used for feature extraction in the proposed method. Experimental results with CASIA database show x % improvement in terms of correct classification performance in comparison to the conventional method.
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