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Multimodal Biometrics Fusion for Human Recognition in Video

Multimodal Biometrics Fusion for Human Recognition in Video

Xiaoli Zhou, Bir Bhanu
Copyright: © 2010 |Pages: 34
ISBN13: 9781605667256|ISBN10: 1605667250|ISBN13 Softcover: 9781616924041|EISBN13: 9781605667263
DOI: 10.4018/978-1-60566-725-6.ch019
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MLA

Zhou, Xiaoli, and Bir Bhanu. "Multimodal Biometrics Fusion for Human Recognition in Video." Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, IGI Global, 2010, pp. 414-447. https://doi.org/10.4018/978-1-60566-725-6.ch019

APA

Zhou, X. & Bhanu, B. (2010). Multimodal Biometrics Fusion for Human Recognition in Video. In L. Wang & X. Geng (Eds.), Behavioral Biometrics for Human Identification: Intelligent Applications (pp. 414-447). IGI Global. https://doi.org/10.4018/978-1-60566-725-6.ch019

Chicago

Zhou, Xiaoli, and Bir Bhanu. "Multimodal Biometrics Fusion for Human Recognition in Video." In Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, 414-447. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-725-6.ch019

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Abstract

This chapter introduces a new video based recognition system to recognize noncooperating individuals at a distance in video, who expose side views to the camera. Information from two biometric sources, side face and gait, is utilized and integrated for recognition. For side face, an enhanced side face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the gait energy image (GEI), a spatiotemporal compact representation of gait in video, is used to characterize human walking properties. The features of face and gait are extracted from ESFI and GEI, respectively. They are integrated at both of the match score level and the feature level by using different fusion strategies. The system is tested on a database of video sequences, corresponding to 45 people, which are collected over several months. The performance of different fusion methods are compared and analyzed. The experimental results show that (a) the idea of constructing ESFI from multiple frames is promising for human recognition in video and better face features are extracted from ESFI compared to those from the original side face images; (b) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI; (c) integrated information from side face and gait is effective for human recognition in video. The feature level fusion methods achieve better performance than the match score level methods fusion overall.

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