Development of deep learning-based facial expression recognition system
Authors:
- 1 January 2015
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-4
Abstract
Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.Keywords
This publication has 5 references indexed in Scilit:
- Learning Expressionlets on Spatio-temporal Manifold for Dynamic Facial Expression Recognition2014
- The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression2010
- A 3-dimensional sift descriptor and its application to action recognition2007
- Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial ExpressionsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
- Robust Real-Time Face DetectionInternational Journal of Computer Vision, 2004