Soumyadip Sengupta, Udit Halder, R. Panda, A. S. Chowdhury
{"title":"A frequency domain approach to silhouette based gait recognition","authors":"Soumyadip Sengupta, Udit Halder, R. Panda, A. S. Chowdhury","doi":"10.1109/NCVPRIPG.2013.6776261","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a frequency domain based model-free gait recognition approach from silhouette inputs using Fourier Transform. Gait sequences are first converted into frequency domain using Fourier transform. Information content of the frequency components are analysed next to determine the number of effective frequencies which can help in the recognition process. These principal frequencies are treated separately to obtain scores based on the correlation coefficient between the gallery and the probe images. The individual scores are fused in the last stage to obtain the final score. The proposed approach is compared with other state-of-the-art model-free gait recognition algorithms. Experimental results on the USF HumanID database clearly indicate the supremacy of our technique.","PeriodicalId":436402,"journal":{"name":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NCVPRIPG.2013.6776261","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In this paper, we propose a frequency domain based model-free gait recognition approach from silhouette inputs using Fourier Transform. Gait sequences are first converted into frequency domain using Fourier transform. Information content of the frequency components are analysed next to determine the number of effective frequencies which can help in the recognition process. These principal frequencies are treated separately to obtain scores based on the correlation coefficient between the gallery and the probe images. The individual scores are fused in the last stage to obtain the final score. The proposed approach is compared with other state-of-the-art model-free gait recognition algorithms. Experimental results on the USF HumanID database clearly indicate the supremacy of our technique.