Fusion of multiple gait cycles for human identification

Sungjun Hong, Heesung Lee, Euntai Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this paper, a gait recognition system fusing multiple gait cycles is presented for human identification. First, the cycle length is estimated by calculating the autocorrelation of the foreground sum signal. After gait cycle partitioning, we extract two kinds of gait feature, gait energy image (GEI) and motion silhouette image (MSI). To identify individual, the outputs of the nearest neighbor classifiers are fused at the abstract level based on majority voting. Our proposed system is tested on the CASIA gait dataset A and the SOTON gait database. Compared to previous works, our empirical results show extraordinary performance in terms of correct classification rate.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages3171-3175
Number of pages5
Publication statusPublished - 2009 Dec 1
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 2009 Aug 182009 Aug 21

Publication series

NameICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CountryJapan
CityFukuoka
Period09/8/1809/8/21

Fingerprint

Autocorrelation
Classifiers
Fusion reactions

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Hong, S., Lee, H., & Kim, E. (2009). Fusion of multiple gait cycles for human identification. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 3171-3175). [5334059] (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).
Hong, Sungjun ; Lee, Heesung ; Kim, Euntai. / Fusion of multiple gait cycles for human identification. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 3171-3175 (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).
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Hong, S, Lee, H & Kim, E 2009, Fusion of multiple gait cycles for human identification. in ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings., 5334059, ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings, pp. 3171-3175, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, Japan, 09/8/18.

Fusion of multiple gait cycles for human identification. / Hong, Sungjun; Lee, Heesung; Kim, Euntai.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 3171-3175 5334059 (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - In this paper, a gait recognition system fusing multiple gait cycles is presented for human identification. First, the cycle length is estimated by calculating the autocorrelation of the foreground sum signal. After gait cycle partitioning, we extract two kinds of gait feature, gait energy image (GEI) and motion silhouette image (MSI). To identify individual, the outputs of the nearest neighbor classifiers are fused at the abstract level based on majority voting. Our proposed system is tested on the CASIA gait dataset A and the SOTON gait database. Compared to previous works, our empirical results show extraordinary performance in terms of correct classification rate.

AB - In this paper, a gait recognition system fusing multiple gait cycles is presented for human identification. First, the cycle length is estimated by calculating the autocorrelation of the foreground sum signal. After gait cycle partitioning, we extract two kinds of gait feature, gait energy image (GEI) and motion silhouette image (MSI). To identify individual, the outputs of the nearest neighbor classifiers are fused at the abstract level based on majority voting. Our proposed system is tested on the CASIA gait dataset A and the SOTON gait database. Compared to previous works, our empirical results show extraordinary performance in terms of correct classification rate.

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Hong S, Lee H, Kim E. Fusion of multiple gait cycles for human identification. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 3171-3175. 5334059. (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).