Facial Expression Recognition using Convolutional Neural Networks: State of the Art

Abstract

The ability to recognize facial expressions automatically enables novel applications in human-computer interaction and other areas. Consequently, there has been active research in this field, with several recent works utilizing Convolutional Neural Networks (CNNs) for feature extraction and inference. These works differ significantly in terms of CNN architectures and other factors. Based on the reported results alone, the performance impact of these factors is unclear. In this paper, we review the state of the art in image-based facial expression recognition using CNNs and highlight algorithmic differences and their performance impact. On this basis, we identify existing bottlenecks and consequently directions for advancing this research field. Furthermore, we demonstrate that overcoming one of these bottlenecks - the comparatively basic architectures of the CNNs utilized in this field - leads to a substantial performance increase. By forming an ensemble of modern deep CNNs, we obtain a FER2013 test accuracy of 75.2%, outperforming previous works without requiring auxiliary training data or face registration.

References in corpus (2)

• Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?
• Learning Social Relation Traits from Face Images

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• A Spontaneous Driver Emotion Facial Expression (DEFE) Dataset for Intelligent Vehicles
• Research on facial expression recognition based on Multimodal data fusion and neural network
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• Training and Profiling a Pediatric Emotion Recognition Classifier on Mobile Devices
• Face-Cap: Image Captioning using Facial Expression Analysis
• Recognizing Facial Expressions in the Wild using Multi-Architectural Representations based Ensemble Learning with Distillation
• Facial Expressions Recognition with Convolutional Neural Networks
• Bayesian Convolutional Neural Networks for Seven Basic Facial Expression Classifications
• Real-Time Facial Expression Emoji Masking with Convolutional Neural Networks and Homography