Multi-Network Fusion Based on CNN for Facial Expression Recognition
Chao Li, Ning Ma, Yalin Deng
Available Online February 2018.
- https://doi.org/10.2991/csece-18.2018.35How to use a DOI?
- facial expression recognition; fusion; multi-network; CNN; SVM
- We propose a method which is multi-network fusion (MNF) based on CNN to recognize facial expressions. Our experimental data adopts the ICML2013 facial expression recognition contest's dataset (FER-2013) and JAFFE dataset. Based on the classic Tang's network structure and Caffe-ImageNet structure, we perform pre-training separately to extract the optimal initialization parameters which are applied for the MNF. We adjust the MNF's parameters through fine-tuning and use L2-SVM for classification. Our experiment has achieved a high accuracy, and the result shows that the effect of the MNF is more obvious than a single network on the facial expression recognition. In this paper, we will describe the specific MNF structure and our training process, as well as the accuracy on the test set.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Chao Li AU - Ning Ma AU - Yalin Deng PY - 2018/02 DA - 2018/02 TI - Multi-Network Fusion Based on CNN for Facial Expression Recognition BT - 2018 International Conference on Computer Science, Electronics and Communication Engineering (CSECE 2018) PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/csece-18.2018.35 DO - https://doi.org/10.2991/csece-18.2018.35 ID - Li2018/02 ER -