Facial Expression Recognition Based on Optimized ResNet

Zhong, Yexiu; Qiu, Senhui; Xiao-Shu, Luo; Meng, Zhiming; Liu, Junxiu

doi:10.1109/wsai49636.2020.9143287

Cited by 15 publications

(9 citation statements)

References 26 publications

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“…In the second stage, we designed three different experiments based on different number of emotion classes as discussed in Section V-C2 to compare our method with the existing approaches on the CK+ dataset. It is worth noticing that the proposed model correctly classifies all the testing images in experiments 1 and 2 as compared to [1], [13], [15] and [16].…”

Section: Comparative Studymentioning

confidence: 98%

“…The model achieved an accuracy of 93.75% on the JAFFE dataset and 94.84% on CK+ dataset. Zhong et al [15] proposed an efficient and simplified model named SE-SResNet18 based on Residual Network (ResNet18) and Squeeze-and-Excitation (SENet). Performance of SE-SResNet18 [15] was checked on CK+ and FER-2013 datasets and achieved an accuracy of 74.143% with resized images of 44 × 44 pixels on FER-2013.…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

“…Zhong et al [15] proposed an efficient and simplified model named SE-SResNet18 based on Residual Network (ResNet18) and Squeeze-and-Excitation (SENet). Performance of SE-SResNet18 [15] was checked on CK+ and FER-2013 datasets and achieved an accuracy of 74.143% with resized images of 44 × 44 pixels on FER-2013. Accuracy of 95.253% was achieved on CK+ dataset with 10-fold validation.…”

Section: B Deep Learning Modelsmentioning

confidence: 99%

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Efficient-SwishNet Based System for Facial Emotion Recognition

et al. 2022

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Facial emotion recognition (FER) is an important research area in artificial intelligence and has many applications i.e., face authentication systems, e-learning, entertainment, deepfakes detection, etc. FER is still a challenging task due to more intra-class variations of emotions. Although existing deep learning methods have achieved good performance for FER. However, still there exists a need to develop efficient and effective FER systems robust to certain conditions i.e., variations in illumination, face angles, gender, race, background settings and people belonging to diverse geographical regions. Moreover, a generalized model for classification of human emotions is required to be implemented in computer systems so that they can interact with humans according to their emotions and improve their interaction. This work presents a novel light-weight Efficient-SwishNet model for emotion recognition that is robust towards the aforementioned conditions. We have introduced a low-cost, smooth unbounded above and bounded below Swish activation function in our model. Property of unboundedness helps to avoid saturation while smoothing helps in optimization and generalization of the model. Performance of the proposed model is evaluated on five diverse datasets including CK+, JAFFE, FER-2013, KDEF and FERG datasets. We also performed cross-corpora evaluation to show the generalizability of our model. The proposed model achieves very high recognition rate for all datasets that proves the merit of the proposed framework for both the human facial images and stylized cartoon characters. Moreover, we conducted an ablation study with different variants of our model to prove its efficiency and effectiveness for emotions identification.

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Section: Comparative Studymentioning

confidence: 98%

Section: B Deep Learning Modelsmentioning

confidence: 99%

Section: B Deep Learning Modelsmentioning

confidence: 99%

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Efficient-SwishNet Based System for Facial Emotion Recognition

et al. 2022

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“…Among them, ResNet [17] used the principle of identity path to further deepen the network without gradient explosion. e CNN [25] model used a parallel convolutional neural network model. e facial expression recognition model designed in this paper had a higher precision and recall.…”

Section: Jaffe Dataset Experimentmentioning

confidence: 99%

“…On the basis of VGG19, the network structure and parameters were optimized to improve the precision. Zhong et al [25] introduced the dropout layer on the basis of ResNet and modified the fully connected layer (FC) to reduce parameters. At the same time, SE block [26] was added to the network to achieve a higher accuracy.…”

Section: Introductionmentioning

confidence: 99%

Facial Expression Recognition Method Combined with Attention Mechanism

Chen

Cheng²,

Zhang

et al. 2021

Mobile Information Systems

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Aiming at the slow speed and low accuracy of traditional facial expression recognition, a new method combining the attention mechanism is proposed. Firstly, group convolution is used to reduce network parameters. The channels of traditional convolution are grouped to cut off redundant connections so that the number of parameters decreases significantly. Secondly, the ERFNet network model was improved by combining the asymmetric residual module and the weak bottleneck module to improve the running speed and reduce the loss of accuracy. Finally, the attention mechanism was added into the feature extraction network to improve the recognition precision. The experiment shows that compared with traditional face recognition methods, the proposed method can improve the recognition precision and recall significantly; in CK+, Jaffe, and Fer2013 datasets, the recognition precision can reach 88.81%, 82.16%, and 79.33%, respectively.

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