TY - JOUR
T1 - Four-layer ConvNet to facial emotion recognition with minimal epochs and the significance of data diversity
AU - Debnath, Tanoy
AU - Reza, Md. Mahfuz
AU - Rahman, Anichur
AU - Beheshti, Amin
AU - Band, Shahab S.
AU - Alinejad-Rokny, Hamid
N1 - Copyright the Crown 2022. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.
PY - 2022
Y1 - 2022
N2 - Emotion recognition is 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 communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study 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 at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system’s success by identifying facial expressions in 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. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.
AB - Emotion recognition is 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 communication, and many more. This paper proposes a new facial emotional recognition model using a convolutional neural network. Our proposed model, “ConvNet”, detects seven specific emotions from image data including anger, disgust, fear, happiness, neutrality, sadness, and surprise. The features extracted by the Local Binary Pattern (LBP), region based Oriented FAST and rotated BRIEF (ORB) and Convolutional Neural network (CNN) from facial expressions images were fused to develop the classification model through training by our proposed CNN model (ConvNet). Our method can converge quickly and achieves good performance which the authors can develop a real-time schema that can easily fit the model and sense emotions. Furthermore, this study 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 at first, and then apply the generalization techniques to the JAFFE and CK+ datasets respectively in the testing stage to evaluate the performance of the model. In the generalization approach on the JAFFE dataset, we get a 92.05% accuracy, while on the CK+ dataset, we acquire a 98.13% accuracy which achieve the best performance among existing methods. We also test the system’s success by identifying facial expressions in 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. However, when compared to other validation methods, the suggested technique was more accurate. ConvNet also achieved validation accuracy of 91.01% for the FER2013 dataset. We also made all the materials publicly accessible for the research community at: https://github.com/Tanoy004/Emotion-recognition-through-CNN.
UR - http://www.scopus.com/inward/record.url?scp=85129016010&partnerID=8YFLogxK
U2 - 10.1038/s41598-022-11173-0
DO - 10.1038/s41598-022-11173-0
M3 - Article
C2 - 35484318
AN - SCOPUS:85129016010
SN - 2045-2322
VL - 12
SP - 1
EP - 18
JO - Scientific Reports
JF - Scientific Reports
M1 - 6991
ER -