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Theoretical Foundation and GPU Implementation of Face Recognition

Theoretical Foundation and GPU Implementation of Face Recognition

William Dixon, Nathaniel Powers, Yang Song, Tolga Soyata
Copyright: © 2015 |Pages: 20
ISBN13: 9781466686625|ISBN10: 1466686626|EISBN13: 9781466686632
DOI: 10.4018/978-1-4666-8662-5.ch011
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MLA

Dixon, William, et al. "Theoretical Foundation and GPU Implementation of Face Recognition." Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, edited by Tolga Soyata, IGI Global, 2015, pp. 322-341. https://doi.org/10.4018/978-1-4666-8662-5.ch011

APA

Dixon, W., Powers, N., Song, Y., & Soyata, T. (2015). Theoretical Foundation and GPU Implementation of Face Recognition. In T. Soyata (Ed.), Enabling Real-Time Mobile Cloud Computing through Emerging Technologies (pp. 322-341). IGI Global. https://doi.org/10.4018/978-1-4666-8662-5.ch011

Chicago

Dixon, William, et al. "Theoretical Foundation and GPU Implementation of Face Recognition." In Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, edited by Tolga Soyata, 322-341. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8662-5.ch011

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Abstract

Enabling a machine to detect and recognize faces requires significant computational power. This particular system of face recognition makes use of OpenCV (Computer Vision) libraries while leveraging Graphics Processing Units (GPUs) to accelerate the process towards real-time. The processing and recognition algorithms are best sorted into three distinct steps: detection, projection, and search. Each of these steps has unique computational characteristics and requirements driving performance. In particular, the detection and projection processes can be accelerated significantly with GPU usage due to the data types and arithmetic types associated with the algorithms, such as matrix manipulation. This chapter provides a survey of the three main processes and how they contribute to the overarching recognition process.

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