Abstract
Face recognition is very important in many applications including surveillance, biometrics, and other domains. Fast face recognition is required if she wants to train or test more images or to increase the resolution of an input image for better accuracy in the recognition. Meanwhile, Graphics Processing Units (GPUs) have become widely available, offering the opportunity for real-time face recognition even for larger set of images with a high resolution. In this paper, we explore the design space of parallelizing a PCA (Principal Component Analysis) based face recognition algorithm and propose a fast face recognizer on GPUs by exploiting the fine-grained data-parallelism found in the face recognition algorithm. We successfully accelerated the major three tasks by 120-folds, 70-folds, and 110-folds, compared to a sequential C implementation. For the end-to-end comparison, our CUDA face recognizer achieved a 30-fold speedup.
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