Deep Learning For Smile Recognition

  title={Deep Learning For Smile Recognition},
  author={Patrick O. Glauner},
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… 
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  • Retrieved: April
  • 2015


  • IEEE Signal Processing Magazine