eXnet: An Efficient Approach for Emotion Recognition in the Wild

Yao Shen · Minyi Guo

72 citations0/0 files storedSensors

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

Abstract

Facial expression recognition has been well studied for its great importance in the areasof human-computer interaction and social sciences. With the evolution of deep learning, therehave been significant advances in this area that also surpass human-level accuracy. Althoughthese methods have achieved good accuracy, they are still suffering from two constraints (high computational power and memory), which are incredibly critical for small hardware-constrained devices. To alleviate this issue, we propose a new Convolutional Neural Network (CNN) architecture eXnet (Expression Net) based on parallel feature extraction which surpasses current methodsin accuracy and contains a much smaller number of parameters (eXnet: 4.57 million, VGG19:14.72 million), making it more efficient and lightweight for real-time systems. Several moderndata augmentation techniques are applied for generalization of eXnet; these techniques improvethe accuracy of the network by overcoming the problem of overfitting while containing the same size. We provide an extensive evaluation of our network against key methods on Facial ExpressionRecognition 2013 (FER-2013), Extended Cohn-Kanade Dataset (CK+), and Real-world Affective Faces Database (RAF-DB) benchmark datasets. We also perform ablation evaluation to show the importance of different components of our architecture. To evaluate the efficiency of eXnet on embedded systems,we deploy it on Raspberry Pi 4B. All these evaluations show the superiority of eXnet for emotionrecognition in the wild in terms of accuracy, the number of parameters, and size on disk.

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References

2016Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order 2018Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition 2019Extended deep neural network for facial emotion recognition 2019Using CNN for facial expression recognition: a study of the effects of kernel size and number of filters on accuracy 2018Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)2018EEG-Based Emotion Recognition using 3D Convolutional Neural Networks 2018Emotion Classification with Data Augmentation Using Generative Adversarial Networks 2019Facial Expression Recognition: A Survey

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2021Dual integrated convolutional neural network for real-time facial expression recognition in the wild 2022Driver Emotions Recognition Based on Improved Faster R-CNN and Neural Architectural Search Network 2021DRER: Deep Learning–Based Driver’s Real Emotion Recognizer 2023Enhancing Facial Expression Recognition System in Online Learning Context Using Efficient Deep Learning Model

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