Reference : Deep Learning For Smile Recognition
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
Deep Learning For Smile Recognition
Glauner, Patrick mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Proceedings of the 12th International FLINS Conference (FLINS 2016)
12th Conference on Uncertainty Modelling in Knowledge Engineering and Decision Making (FLINS 2016)
from 24-08-2016 to 26-08-2016
[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.

File(s) associated to this reference

Fulltext file(s):

Open access
Deep Learning For Smile Recognition.pdfPublisher postprint254.43 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.