Reference : Deep Learning For Smile Recognition
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/28367
Deep Learning For Smile Recognition
English
Glauner, Patrick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
2016
Proceedings of the 12th International FLINS Conference (FLINS 2016)
Yes
International
12th Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016)
from 24-08-2016 to 26-08-2016
Roubaix
France
[en] Computer Vision ; Deep Learning ; Facial expression recognition ; GPU acceleration
[en] 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.
http://hdl.handle.net/10993/28367

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