Facial Expression Recognition Using Computer Vision: A Systematic Review

Canedo, Daniel; Neves, António J. R.

doi:10.3390/app9214678

Cited by 115 publications

(61 citation statements)

References 161 publications

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“…Jung and associates 7 investigated FER with a profound learning approach, which integrates two deep networks that derive faces appearance (using convolutional layers) and geometric features from face landmarks (using completely linked layers), with a 97.3 percent accuracy of CK+ findings. The authors suggested a computer vision FER method in 31 . In the process, the gray-scale face picture was consolidated into a 3-channel input with the corresponding basic LBP and an average LBP feature map.…”

Section: Literature Reviewsmentioning

confidence: 99%

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Debnath¹,

Reza²,

Rahman³

et al. 2021

Preprint

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Emotion recognition defined as identifying human emotion and is directly related to different fields such as human-computer interfaces, human emotional processing, irrational analysis, medical diagnostics, data-driven animation, human-robot communi- cation and many more. The purpose of this study is to propose a new facial emotional recognition model using convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. This research focuses on the model’s training accuracy in a short number of epoch which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this work focuses on the mental or emotional stuff of a man or woman using the behavioral aspects. To complete the training of the CNN network model, we use the FER2013 databases, and we test the system’s success by identifying facial expressions in the real-time. ConvNet consists of four layers of convolution together with two fully connected layers. The experimental results show that the ConvNet is able to achieve 96% training accuracy which is much better than current existing models. ConvNet also achieved validation accuracy of 65% to 70% (considering different datasets used for experiments), resulting in a higher classification accuracy compared to other existing models. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.

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Section: Literature Reviewsmentioning

confidence: 99%

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Debnath¹,

Reza²,

Rahman³

et al. 2021

Preprint

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show abstract

“…A detailed and complete description of facial expression and analysis is given by References [29,30]. In Reference [31], the author gave a brief history of the importance of FER. They gave a complete review of the published number of papers in research journals and conferences from 2006-2019.…”

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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“…Our main concern in the performance evaluation experiments of this paper was neither to evaluate the performance of any specific algorithm, nor to compare the performance of different kinds of algorithm, but to evaluate the overall performance of our suggested all-in-one system of USVS with SOUL in UTOPIA. Detailed information regarding experiments, experiment results, and the performances of various algorithms and comparison among various kinds of algorithms is also well explained in [45][46][47][48][49][50][51]. Figure 8 shows the flowchart of the temporal difference method used for our experiments.…”

Section: Performancementioning

confidence: 99%

A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

et al. 2020

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A smart city is a future city that enables citizens to enjoy Information and Communication Technology (ICT) based smart services with any device, anytime, anywhere. It heavily utilizes Internet of Things. It includes many video cameras to provide various kinds of services for smart cities. Video cameras continuously feed big video data to the smart city system, and smart cities need to process the big video data as fast as it can. This is a very challenging task because big computational power is required to shorten processing time. This paper introduces UTOPIA Smart Video Surveillance, which analyzes the big video images using MapReduce, for smart cities. We implemented the smart video surveillance in our middleware platform. This paper explains its mechanism, implementation, and operation and presents performance evaluation results to confirm that the system worked well and is scalable, efficient, reliable, and flexible.

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Facial Expression Recognition Using Computer Vision: A Systematic Review

Cited by 115 publications

References 161 publications

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

Four-Layer ConvNet to Facial Emotion Recognition with Minimal Epochs and the Significance of Data Diversity

eXnet: An Efficient Approach for Emotion Recognition in the Wild

A Cloud-Based UTOPIA Smart Video Surveillance System for Smart Cities

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