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Face Recognition: A Tutorial on Computational Aspects

Alexander Alling, Nathaniel Powers, Tolga Soyata
Dept. of Electrical and Computer Engineering, University of Rochester
University of Rochester, 2015

@article{alling2015face,

   title={Face Recognition: A Tutorial on Computational Aspects},

   author={Alling, Alexander and Powers, Nathaniel and Soyata, Tolga},

   year={2015}

}

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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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