Facial Expression Recognition with CNN Ensemble

Kuang Liu · Mingmin Zhang · Zhigeng Pan

188 citations0/0 files storedInternational Conference on Cyberworlds (CW)

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

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.

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2016Going deeper in facial expression recognition using deep neural networks

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2021Facial Expression Recognition Using Residual Masking Network 2019Three convolutional neural network models for facial expression recognition in the wild 2020Pyramid With Super Resolution for In-the-Wild Facial Expression Recognition 2021Facial Emotion Recognition: State of the Art Performance on FER2013 2020A Lightweight Convolutional Neural Network for Real-Time Facial Expression Detection 2023A novel facial emotion recognition model using segmentation VGG-19 architecture 2017Facial emotion recognition on a dataset using convolutional neural network 2021A Review on Finding Efficient Approach to Detect Customer Emotion Analysis using Deep Learning Analysis

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