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Deep Learning For Smile Recognition

Patrick O. Glauner
Interdisciplinary Centre for Security, Reliability and Trust, University of Luxembourg, 2721 Luxembourg, Luxembourg
arXiv:1602.00172 [cs.CV], (30 Jan 2016)

@article{glauner2016deep,

   title={Deep Learning For Smile Recognition},

   author={Glauner, Patrick O.},

   year={2016},

   month={jan},

   archivePrefix={"arXiv"},

   primaryClass={cs.CV}

}

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Inspired by recent successes of deep learning in computer vision, we propose a novel application of deep convolutional neural networks to facial expression recognition, in particular smile recognition. A smile recognition test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches based on hand-crafted features with accuracies ranging from 65.55% to 79.67%. The novelty of this approach includes a comprehensive model selection of the architecture parameters, allowing to find an appropriate architecture for each expression such as smile. This is feasible because all experiments were run on a Tesla K40c GPU, allowing a speedup of factor 10 over traditional computations on a CPU.
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