Deep Learning For Smile Recognition

Patrick O. Glauner

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arXiv · 1602.00172

Deep Learning For Smile Recognition

Patrick O. Glauner

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Abstract

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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Patrick O. Glauner. 2016-01-30. Deep Learning For Smile Recognition. https://arxiv.org/abs/1602.00172

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