Context-aware fusion: A case study on fusion of gait and face for human identification in video

Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules t...

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Published inPattern recognition Vol. 43; no. 10; pp. 3660 - 3673
Main Authors Geng, Xin, Smith-Miles, Kate, Wang, Liang, Li, Ming, Wu, Qiang
Format Journal Article
LanguageEnglish
Published Kidlington Elsevier Ltd 01.10.2010
Elsevier
Subjects
Applied sciences
Biometrics
Context-awareness
Exact sciences and technology
Face recognition
Gait
Gait recognition
Human
Human identification
Image processing
Information, signal and communications theory
Machine learning
Miscellaneous
Multi-biometric fusion
Pattern recognition
Real time
Signal processing
Telecommunications and information theory
Context-awareness
Human identification
Face recognition
Multi-biometric fusion
Gait recognition
Biometrics
Image processing
Adaptive system
Identification
Pattern recognition
Context aware
Gait analysis
Learning
Case study
Minimax method
Signal processing
Automatic recognition
Motion analysis
Online AccessGet full text
ISSN0031-3203
1873-5142
DOI10.1016/j.patcog.2010.04.012

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Abstract Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge.
AbstractList Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the environment or the individual users. This paper proposes context-aware multi-biometric fusion, which can dynamically adapt the fusion rules to the real-time context. As a typical application, the context-aware fusion of gait and face for human identification in video is investigated. Two significant context factors that may affect the relationship between gait and face in the fusion are considered, i.e., view angle and subject-to-camera distance. Fusion methods adaptable to these two factors based on either prior knowledge or machine learning are proposed and tested. Experimental results show that the context-aware fusion methods perform significantly better than not only the individual biometric traits, but also those widely adopted static fusion rules including SUM, PRODUCT, MIN, and MAX. Moreover, context-aware fusion based on machine learning shows superiority over that based on prior knowledge.
Author Li, Ming
Geng, Xin
Wang, Liang
Smith-Miles, Kate
Wu, Qiang
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Issue 10
Keywords Context-awareness
Human identification
Face recognition
Multi-biometric fusion
Gait recognition
Biometrics
Image processing
Adaptive system
Identification
Pattern recognition
Context aware
Gait analysis
Learning
Case study
Minimax method
Signal processing
Automatic recognition
Motion analysis
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Snippet Most work on multi-biometric fusion is based on static fusion rules. One prominent limitation of static fusion is that it cannot respond to the changes of the...
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SubjectTerms Applied sciences
Biometrics
Context-awareness
Exact sciences and technology
Face recognition
Gait
Gait recognition
Human
Human identification
Image processing
Information, signal and communications theory
Machine learning
Miscellaneous
Multi-biometric fusion
Pattern recognition
Real time
Signal processing
Telecommunications and information theory
Title Context-aware fusion: A case study on fusion of gait and face for human identification in video
URI https://dx.doi.org/10.1016/j.patcog.2010.04.012
https://www.proquest.com/docview/753681539
Volume 43
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