Facial Emotion Recognition: State of the Art Performance on FER2013

Abstract

Facial emotion recognition (FER) is significant for human-computer interaction such as clinical practice and behavioral description. Accurate and robust FER by computer models remains challenging due to the heterogeneity of human faces and variations in images such as different facial pose and lighting. Among all techniques for FER, deep learning models, especially Convolutional Neural Networks (CNNs) have shown great potential due to their powerful automatic feature extraction and computational efficiency. In this work, we achieve the highest single-network classification accuracy on the FER2013 dataset. We adopt the VGGNet architecture, rigorously fine-tune its hyperparameters, and experiment with various optimization methods. To our best knowledge, our model achieves state-of-the-art single-network accuracy of 73.28 % on FER2013 without using extra training data.

9 pages, 5 figures, 2 tables

References in corpus (5)

• Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
• Facial Expression Recognition using Convolutional Neural Networks: State of the Art
• Optimization for deep learning: theory and algorithms
• Learning to Amend Facial Expression Representation via De-albino and Affinity
• An Exponential Learning Rate Schedule for Deep Learning

Cited by in corpus (5)

• GA2MIF: Graph and Attention Based Two-Stage Multi-Source Information Fusion for Conversational Emotion Detection
• EmoNeXt: an Adapted ConvNeXt for Facial Emotion Recognition
• Significance tests of feature relevance for a black-box learner
• Evaluation of Conversational Agents: Understanding Culture, Context and Environment in Emotion Detection
• The Face of Populism: Examining Differences in Facial Emotional Expressions of Political Leaders Using Machine Learning