Facial Expression Recognition Based on Convolution Neural Network
Yue Duan, Linli Zhou, Yue Wu
Available Online July 2016.
- https://doi.org/10.2991/iccia-17.2017.55How to use a DOI?
- Facial expression recognition, convolutional neural networks, deep learning, graphics processing unit, feature extraction.
- With the popularity of computer technology in people's daily life, facial expression recognition in the human-computer interaction, home entertainment, public safety and even medical applications in the field more and more widely. In recent decades, the rapid development of deep learning areas has brought new opportunities for breakthroughs in various fields. Unlike traditional methods of extracting features manually, researchers can obtain the characteristics of automatic learning and generalization through the method of machine learning. To avoid the complex explicit feature extraction process in traditional expression recognition, a convolutional neural network (CNN) for the facial expression recognition is proposed. Firstly, the facial expression image is normalized and the implicit features are extracted by using the trainable convolution kernel. Then, the maximum pooling is used to reduce the dimensions of the extracted implicit features. Finally, the Soft max classifier is used to classify the facial expressions of the test samples. Experimental results show the performance and the generalization ability of the CNN for facial expression recognition.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Yue Duan AU - Linli Zhou AU - Yue Wu PY - 2016/07 DA - 2016/07 TI - Facial Expression Recognition Based on Convolution Neural Network BT - 2nd International Conference on Computer Engineering, Information Science & Application Technology (ICCIA 2017) PB - Atlantis Press SP - 327 EP - 331 SN - 2352-538X UR - https://doi.org/10.2991/iccia-17.2017.55 DO - https://doi.org/10.2991/iccia-17.2017.55 ID - Duan2016/07 ER -