Deep Learning For Smile Recognition
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.
Cite
@article{arxiv.1602.00172,
title = {Deep Learning For Smile Recognition},
author = {Patrick O. Glauner},
journal= {arXiv preprint arXiv:1602.00172},
year = {2017}
}
Comments
Proceedings of the 12th Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016)