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 | Schools: | School of Electrical and Electronic Engineering | Rights: | © 2013 IEEE. | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Journal Articles |
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