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GPU Acceleration of k-Nearest Neighbor Search in Face Classifier based on Eigenfaces

Jennifer Dawn Rouan
University of Georgia
University of Georgia, 2014

@article{rouan2014gpu,

   title={GPU Acceleration of k-Nearest Neighbor Search in Face Classifier based on Eigenfaces},

   author={Rouan, Jennifer Dawn},

   year={2014}

}

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Face recognition is a specialized case of object recognition, and has broad applications in security, surveillance, identity management, law enforcement, human-computer interaction, and automatic photo and video indexing. Because human faces occupy a narrow portion of the total image space, specialized methods are required to identify faces based on subtle differences. One such method is the Eigenfaces classifier, a holistic statistical method which significantly reduces the dimensionality of the search space. Despite the dimensionality reduction, the k-nearest neighbor (kNN) search remains a bottleneck in this application as well as most others that use it. The kNN search is both computationally complex and highly data-parallel, making it a candidate for acceleration on graphics hardware. I present an implementation of an Eigenfaces classifier with a portion of the kNN search accelerated on an Nvidia Tesla K20X GPU with 2688 cores.
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