2017 5th International Workshop on Biometrics and Forensics (IWBF)最新文献

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Template ageing in non-minutiae fingerprint recognition 非细节指纹识别中的模板老化

2017 5th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 2017-04-01 DOI: 10.1109/IWBF.2017.7935091

Simon Kirchgasser, A. Uhl

引用次数: 9

Fast and robust framework for view-invariant gait recognition 快速鲁棒的视觉不变步态识别框架

2017 5th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 1900-01-01 DOI: 10.1109/IWBF.2017.7935092

Ning Jia, Chang-Tsun Li, Victor Sanchez, Alan Wee-Chung Liew

{"title":"Fast and robust framework for view-invariant gait recognition","authors":"Ning Jia, Chang-Tsun Li, Victor Sanchez, Alan Wee-Chung Liew","doi":"10.1109/IWBF.2017.7935092","DOIUrl":"https://doi.org/10.1109/IWBF.2017.7935092","url":null,"abstract":"View-invariant gait recognition is one of the major challenges in identifying people through their gait. Many researchers have evaluated view angle transformation techniques, discriminant analysis and manifold learning approaches for cross-view recognition, and their proposals are usually based on a common factor, i.e., to establish a cross-view mapping between gallery and probe templates. However, their effectiveness is restricted to small view angle variances. A promising approach to perform view-invariant gait recognition is through multi-view feature learning. In this paper, we propose the view-invariant feature selector (ViFS) and integrate it in a framework for view-invariant gait recognition. ViFS select features from multi-view gait templates and reconstructs gallery templates that accurately match the data for a specific view angle. ViFS is thus able to reconstruct gallery templates from arbitrary view angles, and thus help to transfer the cross-view problem to identical-view gait recognition. We also apply linear subspace learning methods as feature enhancers for ViFS, which substantially reduce the computational cost and improve the recognition speed. We test the proposed framework on the CASIA Dataset B. The average recognition accuracy of the proposed framework for 11 different views exceed 98%.","PeriodicalId":111316,"journal":{"name":"2017 5th International Workshop on Biometrics and Forensics (IWBF)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126801649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

An adaptive tri-pixel unit steganographic algorithm using the least two significant bits 一种使用最小两个有效位的自适应三像素单位隐写算法

2017 5th International Workshop on Biometrics and Forensics (IWBF) Pub Date : 1900-01-01 DOI: 10.1109/IWBF.2017.7935085

Xiangyu Yu, Hui-hua Liang, Minting Li, Chang-Tsun Li

引用次数: 2

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