Gait recognition based on Hidden Markov models

Author

Qi Yang

Author_Institution

Sch. of Inf. Sci. & Eng., Northeastern Univ., Shenyang, China

fYear

2011

fDate

19-22 Aug. 2011

Firstpage

1453

Lastpage

1456

Abstract

In this paper, proposed a method for gait recognition based on the HMM. In this method sparse presentation is used and experience is done in CASIA B database, and all the study are based on this database. Firstly, a gait silhouette or feature cycle is divided into several temporally adjacent clusters. Each cluster is calculated and gets several adjacent PFDEI binary images. Secondly, Hidden Markov mode (HMM) is built to describe the relationship among the PFDEI. Finally, sparse representation is applied to analyze binary image to learning its parameters. Experimental results show that such methods to be identified effective.

Keywords

feature extraction; gait analysis; hidden Markov models; image motion analysis; image recognition; learning (artificial intelligence); visual databases; CASIA B database; HMM; adjacent PFDEI binary images; feature cycle; frame difference energy image; gait recognition; gait silhouette; hidden Markov model; sparse presentation; temporally adjacent clusters; Decision support systems; Handheld computers; Mechatronics; HMM; KPCA; PFDEI; dictinary; sparse representation;

fLanguage

English

Publisher

ieee

Conference_Titel

Mechatronic Science, Electric Engineering and Computer (MEC), 2011 International Conference on

Conference_Location

Jilin

Print_ISBN

978-1-61284-719-1

Type

conf

DOI

10.1109/MEC.2011.6025745

Filename

6025745