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|Title:||Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions||Authors:||Lu, Jiwen
|Keywords:||Human gait analysis
|Issue Date:||2013||Source:||Lu, J., Wang, G., & Moulin, P. (2014). Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions. IEEE Transactions on Information Forensics and Security, 9(1), 51-61.||Series/Report no.:||IEEE Transactions on Information Forensics and Security||Abstract:||We investigate the problem of human identity and gender recognition from gait sequences with arbitrary walking directions. Most current approaches make the unrealistic assumption that persons walk along a fixed direction or a pre-defined path. Given a gait sequence collected from arbitrary walking directions, we first obtain human silhouettes by background subtraction and cluster them into several clusters. For each cluster, we compute the cluster-based averaged gait image as features. Then, we propose a sparse reconstruction based metric learning method to learn a distance metric to minimize the intra-class sparse reconstruction errors and maximize the inter-class sparse reconstruction errors simultaneously, so that discriminative information can be exploited for recognition. The experimental results show the efficacy of our approach.||URI:||https://hdl.handle.net/10356/81676
|ISSN:||1556-6013||DOI:||10.1109/TIFS.2013.2291969||Rights:||© 2013 IEEE.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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