Facial Expression Recognition with CNN Ensemble
Kuang Liu, Mingmin Zhang, Zhigeng Pan · 2016
This paper is focusing on the Facial ExpressionRecognition (FER) problem from a single face image. Inspired by the advances Convolutional Neural Networks (CNNs) haveachieved 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 compactCNN 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.