2016/01/30 by Patrick O. Glauner, Glauner, Patrick O. · 1 voice
Computer Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #cs.CV #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.1602.00172
Proceedings of the 12th Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016)
arxiv published 2016/01/30 · arxiv created 2017/07/25 · arxiv updated 2017/07/26
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.