Facial Expression Recognition: A Survey

Huang, Yunxin; Chen, Fei; Lv, Shaohe; Wang, Xiaodong

doi:10.3390/sym11101189

Cited by 145 publications

(83 citation statements)

References 121 publications

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“…Table 9. Relations between emotions and facial expressions [145,146]. The EMG procedure is performed by measuring voltages between special electrodes.…”

Section: Electromyogram (Emg)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

406

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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“…Table 9. Relations between emotions and facial expressions [145,146]. The EMG procedure is performed by measuring voltages between special electrodes.…”

Section: Electromyogram (Emg)mentioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

406

170

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“…According to a recent survey [3], another branch of FER studies uses neural networks for the feature and classifier. For example, Action Unit-inspired deep network (AUDN) [4] is a deep neural network with 95.78% classification accuracy for six facial expression classes in the CK+ database.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition by Regional Weighting with Approximated Q-Learning

Oh¹,

Kim²

2020

Symmetry

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Several facial expression recognition methods cluster facial elements according to similarity and weight them considering the importance of each element in classification. However, these methods are limited by the pre-definitions of units restricting modification of the structure during optimization. This study proposes a modified support vector machine classifier called Grid Map, which is combined with reinforcement learning to improve the classification accuracy. To optimize training, the input image size is normalized according to the cascade rules of a pre-processing detector, and the regional weights are assigned by an adaptive cell size that divides each region of the image using bounding grids. Reducing the size of the bounding grid reduces the area used for feature extraction, allowing more detailed weighted features to be extracted. Error-correcting output codes with a histogram of gradient is selected as the classification method via an experiment to determine the optimal feature and classifier selection. The proposed method is formulated into a decision process and solved via Q-learning. To classify seven emotions, the proposed method exhibits accuracies of 96.36% and 98.47% for four databases and Extended Cohn-–Kanade Dataset (CK+), respectively. Compared to the basic method exhibiting a similar accuracy, the proposed method requires 68.81% fewer features and only 66.33% of the processing time.

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“…They gave a complete review of the published number of papers in research journals and conferences from 2006-2019. Recently, Huang et al [32] published a survey paper on emotion classification that gave the state-of-the-act results of more than eight famous facial expression datasets till 2019. According to this survey article, Reference [19] presents a deep learning method based on facial action units, and they achieved 72% accuracy on the FER-2013 dataset.…”

Section: Related Workmentioning

confidence: 99%

eXnet: An Efficient Approach for Emotion Recognition in the Wild

Riaz

Shen

Sohail

et al. 2020

Sensors

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Facial expression recognition has been well studied for its great importance in the areas of human–computer interaction and social sciences. With the evolution of deep learning, there have been significant advances in this area that also surpass human-level accuracy. Although these 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 methods in 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 modern data augmentation techniques are applied for generalization of eXnet; these techniques improve the 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 Expression Recognition 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 emotion recognition in the wild in terms of accuracy, the number of parameters, and size on disk.

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Facial Expression Recognition: A Survey

Cited by 145 publications

References 121 publications

Human Emotion Recognition: Review of Sensors and Methods

Human Emotion Recognition: Review of Sensors and Methods

Facial Expression Recognition by Regional Weighting with Approximated Q-Learning

eXnet: An Efficient Approach for Emotion Recognition in the Wild

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