CASIA OpenIR  > 中国科学院分子影像重点实验室
Factorial HMM and Parallel HMM for Gait Recognition
Chen, Changhong1; Liang, Jimin1; Zhao, Heng1; Hu, Haihong1; Tian, Jie1,2
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS
2009
Volume39Issue:1Pages:114-123
SubtypeArticle
AbstractInformation fusion offers a promising solution to the development of a high-performance classification system. In this paper, the problem of multiple gait features fusion is explored with the framework of the factorial hidden Markov model (FHMM). The FHMM has a multiple-layer structure and provides an alternative to combine several gait features without concatenating them into a single augmented feature. Besides, the feature concatenation is used to directly concatenate the features and the parallel HMM (PHMM) is introduced as a decision-level fusion scheme, which employs traditional fusion rules to combine the recognition results at decision level. To evaluate the recognition performances, McNemar's test is employed to compare the FHMM feature-level fusion scheme with the feature concatenation and the PHMM decision-level fusion scheme. Statistical numerical experiments are carried out on the Carnegie Mellon University motion of body and the Institute of Automation of the Chinese Academy of Sciences gait databases. The experimental results demonstrate that the FHMM feature-level fusion scheme and the PHMM decision-level fusion scheme outperform feature concatenation. The FHMM feature-level fusion scheme tends to perform better than the PHMM decision-level fusion scheme when only a few gait cycles are available for recognition.
KeywordFactorial Hidden Markov Model (Fhmm) Gait Recognition Information Fusion Mcnemar's Test Parallel Hmm (pHmm) Performance Evaluation
WOS HeadingsScience & Technology ; Technology
WOS KeywordHUMAN IDENTIFICATION ; FUSION ; FACE
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000262328400010
Citation statistics
Cited Times:31[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3936
Collection中国科学院分子影像重点实验室
Corresponding AuthorTian, Jie
Affiliation1.Xidian Univ, Sch Elect Engn, Life Sci Res Ctr, Xian 710071, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100080, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Changhong,Liang, Jimin,Zhao, Heng,et al. Factorial HMM and Parallel HMM for Gait Recognition[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS,2009,39(1):114-123.
APA Chen, Changhong,Liang, Jimin,Zhao, Heng,Hu, Haihong,&Tian, Jie.(2009).Factorial HMM and Parallel HMM for Gait Recognition.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS,39(1),114-123.
MLA Chen, Changhong,et al."Factorial HMM and Parallel HMM for Gait Recognition".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS 39.1(2009):114-123.
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