Gait-Based Recognition of Human Using an Embedded Hidden Markov Models

Author

Zhang Qian-jin ; Xu Su-li

Author_Institution

Sch. of Electron. & Inf. Eng., Henan Univ. of Sci. & Technol., Luoyang, China

fYear

2009

fDate

19-20 Dec. 2009

Firstpage

1

Lastpage

4

Abstract

An embedded hidden Markov models (e-HMM) gait recognition scheme based on gait energy image (GEI) is proposed. First, the mean GEI is calculated from gait periodic, then we analyze the mean GEI regions, making use of the two dimensional discrete cosine transform (2D-DCT) to transfer the regions into observation vector, and complete the e-HMM training and humans recognition. We compare the proposed algorithm with other gait recognition approaches on USF HumanID Database and CASIA Gait Database. Experimental results show that the proposed approach is valid and has encouraging recognition performance.

Keywords

discrete cosine transforms; gait analysis; hidden Markov models; image recognition; CASIA Gait Database; USF HumanID Database; discrete cosine transform; e-HMM training; embedded hidden Markov model; gait energy image; gait periodic; gait recognition; human gait; human recognition; mean GEI region; Discrete cosine transforms; Face recognition; Hidden Markov models; Humans; Image databases; Image recognition; Legged locomotion; Power engineering and energy; Spatial databases; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-4994-1

Type

conf

DOI

10.1109/ICIECS.2009.5365329

Filename

5365329