View-invariant Gait Recognition through Genetic Template Segmentation

Autor:	Isaac, Ebenezer, Elias, Susan, Rajagopalan, Srinivasan, Easwarakumar, K. S.
Rok vydání:	2017
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Zdroj:	IEEE Signal Processing Letters, vol. 24, no. 8, pp. 1188-1192, 2017
Druh dokumentu:	Working Paper
DOI:	10.1109/LSP.2017.2715179
Popis:	Template-based model-free approach provides by far the most successful solution to the gait recognition problem in literature. Recent work discusses how isolating the head and leg portion of the template increase the performance of a gait recognition system making it robust against covariates like clothing and carrying conditions. However, most involve a manual definition of the boundaries. The method we propose, the genetic template segmentation (GTS), employs the genetic algorithm to automate the boundary selection process. This method was tested on the GEI, GEnI and AEI templates. GEI seems to exhibit the best result when segmented with our approach. Experimental results depict that our approach significantly outperforms the existing implementations of view-invariant gait recognition. Comment: Published in IEEE Signal Processing Letters. Received April 24, 2017, revised June 6, 2017, accepted June 10, 2017, published June 14, 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1705.05273 Zobrazit plný text záznamu View this record from Arxiv