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 multibiometric 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 ∗Corresponding author, Telephone: +86 (25) 5209 0876, Fax: +86 (25) 5209 0876 Email addresses: [email protected] (Xin Geng), [email protected] (Kate Smith-Miles), [email protected] (Liang Wang), [email protected] (Ming Li), [email protected] (Qiang Wu) Preprint submitted to Pattern Recognition April 13, 2010 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.