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.
Original language | English (US) |
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Article number | 6671367 |
Pages (from-to) | 51-61 |
Number of pages | 11 |
Journal | IEEE Transactions on Information Forensics and Security |
Volume | 9 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2014 |
Keywords
- Human gait analysis
- gender recognition
- identity recognition
- metric learning
- sparse reconstruction
ASJC Scopus subject areas
- Safety, Risk, Reliability and Quality
- Computer Networks and Communications