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Activity and Individual Human Recognition in Infrared Imagery

Activity and Individual Human Recognition in Infrared Imagery

Bir Bhanu, Ju Han
Copyright: © 2010 |Pages: 13
ISBN13: 9781605667256|ISBN10: 1605667250|EISBN13: 9781605667263
DOI: 10.4018/978-1-60566-725-6.ch011
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MLA

Bhanu, Bir, and Ju Han. "Activity and Individual Human Recognition in Infrared Imagery." Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, IGI Global, 2010, pp. 224-236. https://doi.org/10.4018/978-1-60566-725-6.ch011

APA

Bhanu, B. & Han, J. (2010). Activity and Individual Human Recognition in Infrared Imagery. In L. Wang & X. Geng (Eds.), Behavioral Biometrics for Human Identification: Intelligent Applications (pp. 224-236). IGI Global. https://doi.org/10.4018/978-1-60566-725-6.ch011

Chicago

Bhanu, Bir, and Ju Han. "Activity and Individual Human Recognition in Infrared Imagery." In Behavioral Biometrics for Human Identification: Intelligent Applications, edited by Liang Wang and Xin Geng, 224-236. Hershey, PA: IGI Global, 2010. https://doi.org/10.4018/978-1-60566-725-6.ch011

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Abstract

In this chapter the Authors introduce the concepts behind the mouse dynamics biometric technology, present a generic architecture of the detector used to collect and process mouse dynamics, and study the various factors used to build the user’s signature. The Authors will also provide an updated survey on the researches and industrial implementations related to the technology, and study possible applications in computer security.In this chapter, we investigate repetitive human activity patterns and individual recognition in thermal infrared imagery, where human motion can be easily detected from the background regardless of the lighting conditions and colors of the human clothing and surfaces, and backgrounds. We employ an efficient spatiotemporal representation for human repetitive activity and individual recognition, which represents human motion sequence in a single image while preserving spatiotemporal characteristics. A statistical approach is used to extract features for activity and individual recognition. Experimental results show that the proposed approach achieves good performance for repetitive human activity and individual recognition.

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