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Face Recognition: A Tutorial on Computational Aspects
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Author(s): Alexander Alling (University of Rochester, USA), Nathaniel R. Powers (University of Rochester, USA)and Tolga Soyata (University of Rochester, USA)
Copyright: 2016
Pages: 21
Source title:
Emerging Research Surrounding Power Consumption and Performance Issues in Utility Computing
Source Author(s)/Editor(s): Ganesh Chandra Deka (Regional Vocational Training Institute (RVTI) for Women, India), G.M. Siddesh (Ramaiah Institute of Technology, India), K. G. Srinivasa (M S Ramaiah Institute of Technology, Bangalore, India)and L.M. Patnaik (IISc, Bangalore, India)
DOI: 10.4018/978-1-4666-8853-7.ch020
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Abstract
Face recognition is a sophisticated problem requiring a significant commitment of computer resources. A modern GPU architecture provides a practical platform for performing face recognition in real time. The majority of the calculations of an eigenpicture implementation of face recognition are matrix multiplications. For this type of computation, a conventional computer GPU is capable of computing in tens of milliseconds data that a CPU requires thousands of milliseconds to process. In this chapter, we outline and examine the different components and computational requirements of a face recognition scheme implementing the Viola-Jones Face Detection Framework and an eigenpicture face recognition model. Face recognition can be separated into three distinct parts: face detection, eigenvector projection, and database search. For each, we provide a detailed explanation of the exact process along with an analysis of the computational requirements and scalability of the operation.
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