Facial Expression Recognition with CNN Ensemble

This paper is focusing on the Facial Expression Recognition (FER) problem from a single face image. Inspired by the advances Convolutional Neural Networks (CNNs) have achieved in image recognition and classification, we propose a CNN-based approach to address this problem. Our model consists of seve...

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Bibliographic Details
Published in2016 International Conference on Cyberworlds (CW) pp. 163 - 166
Main Authors Kuang Liu, Minming Zhang, Zhigeng Pan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2016
Subjects
CNN
Convolution
Face
Face recognition
Facial Expression Recognition
Feature extraction
image classification
Image recognition
Neural networks
Training
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Summary:This paper is focusing on the Facial Expression Recognition (FER) problem from a single face image. Inspired by the advances Convolutional Neural Networks (CNNs) have achieved in image recognition and classification, we propose a CNN-based approach to address this problem. Our model consists of several different structured subnets. Each subnet is a compact CNN model trained separately. The whole network is structured by assembling these subnets together. We trained and evaluated our model on the FER2013 dataset[7]. The best single subnet achieved 62.44% accuracy and the whole model scored 65.03% accuracy, which is ranked 9th and 5th respectively among all other participants.
DOI:10.1109/CW.2016.34