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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
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Pattern Recognition Letters
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Pattern Recognition
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CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Human Gait Recognition With Matrix Representation
IEEE Transactions on Circuits and Systems for Video Technology
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
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IEEE Transactions on Information Forensics and Security
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J-HGBU '11 Proceedings of the 2011 joint ACM workshop on Human gesture and behavior understanding
Pattern Recognition Letters
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Information 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.