Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/81676
Title: Human Identity and Gender Recognition From Gait Sequences With Arbitrary Walking Directions
Authors: Lu, Jiwen
Wang, Gang
Moulin, Pierre
Keywords: Human gait analysis
Identity recognition
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
http://hdl.handle.net/10220/40923
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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