| Title | Deep Learning for Real-Time Robust Facial Expression Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Khryashchev, Vladimir, Ivanovsky, Leonid, Priorov, Andrey |
| Conference Name | Proceedings of the International Conference on Machine Vision and Applications |
| Publisher | ACM |
| Conference Location | New York, NY, USA |
| ISBN Number | 978-1-4503-6381-5 |
| Keywords | convolutional neural network, Deep Learning, deep video, facial expression analysis, Metrics, pubcrawl, real-time video analysis, Resiliency, Scalability |
| Abstract | The aim of this investigation is to classify real-life facial images into one of six types of emotions. For solving this problem, we propose to use deep machine learning algorithms and convolutional neural network (CNN). CNN is a modern type of neural network, which allows for rapid detection of various objects, as well as to make an effective object classification. For acceleration of CNN learning stage, we use supercomputer NVIDIA DGX-1. This process was implemented in parallel on a large number of independent streams on GPU. Numerical experiments for algorithms were performed on the images of Multi-Pie image database with various lighting of scene and angle rotation of head. For developed models, several metrics of quality were calculated. The designing algorithm was used in real-time video processing in human-computer interaction systems. Moreover, expression recognition can apply in such fields as retail analysis, security, video games, animations, psychiatry, automobile safety, educational software, etc. |
| URL | http://doi.acm.org/10.1145/3220511.3220518 |
| DOI | 10.1145/3220511.3220518 |
| Citation Key | khryashchev_deep_2018 |
Deep Learning for Real-Time Robust Facial Expression Analysis