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

• Published in: Pattern recognition Vol. 43; no. 10; pp. 3660 - 3673
• Main Authors: Geng, Xin, Smith-Miles, Kate, Wang, Liang, Li, Ming, Wu, Qiang
• Format: Journal Article
• Language: English
• 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
• ISSN: 0031-3203; 1873-5142
• DOI: 10.1016/j.patcog.2010.04.012

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.