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Theoretical Foundation and GPU Implementation of Face Recognition

Theoretical Foundation and GPU Implementation of Face Recognition
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Author(s): William Dixon (University of Rochester, USA), Nathaniel Powers (University of Rochester, USA), Yang Song (University of Rochester, USA)and Tolga Soyata (University of Rochester, USA)
Copyright: 2015
Pages: 20
Source title: Enabling Real-Time Mobile Cloud Computing through Emerging Technologies
Source Author(s)/Editor(s): Tolga Soyata (University of Rochester, USA)
DOI: 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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