Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

Kusuma, Gede Putra; Jonathan, Jonathan; Lim, Andreas Pangestu

doi:10.25046/aj050638

Cited by 37 publications

(7 citation statements)

References 43 publications

(61 reference statements)

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“…Three public datasets for expression recognition technology evaluation are used to validate the model's performance, namely the Ck+ dataset [ 27 ], the Jaffe dataset [ 28 ], and the Fer-2013 dataset [ 29 ]. The Ck+ dataset contains 327 video sequences from 123 subjects.…”

Section: Methodsmentioning

confidence: 99%

Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning

Zhong¹

2022

Computational Intelligence and Neuroscience

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With the development of society, China pays more and more attention to cultural education. The teaching method of introducing ideological and political content into cultural teaching plays an important role in improving the overall teaching quality. However, the traditional methods used to evaluate the quality of culture teaching, curriculum ideological, and political teaching have some problems, such as strong subjectivity and unrepresentative results. Firstly, this work analyzes the connotation of curriculum thought and politics. Secondly, a teaching quality evaluation model based on an improved lightweight convolutional neural network (CNN) is proposed, which mainly judges the students’ recognition of teachers’ content and teaching methods by identifying the students’ expressions in the classroom. Finally, the students of a senior high school in Shanghai are selected as the survey object, and the current situation of ideological and political education (IPE) in the school curriculum is preliminarily understood by issuing a questionnaire; experiments are designed to test the performance of the model. The results show that most of the students in the school do not understand the connotation of IPE, and the teachers cannot accurately and deeply teach the relevant ideological and political knowledge to the students. About 73% and 82% of students prefer that teachers can mention life experience and social skills in class. More than 50% of the students are more willing to accept the course ideological and political activities in the form of lectures and competitions. This indirectly shows that the school lacks the above contents in the current course ideological and political teaching, the teaching method is relatively single, and cannot fully mobilize the enthusiasm of students. Further improvement is needed for these problems in the follow-up. The accuracy of expression recognition of this model is more than 2.9% higher than other algorithms, and the improvement effect of the model is remarkable. To sum up, this work fully understands the current teaching situation of the surveyed schools through questionnaire survey, and puts forward corresponding improvement suggestions. The effectiveness of this model is verified by designing experiments, which proves that it is suitable for the research of teaching quality evaluation.

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Section: Methodsmentioning

confidence: 99%

Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning

Zhong¹

2022

Computational Intelligence and Neuroscience

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“…Therefore, hyperparameter optimization via the CNN-GA approach offers promising prospects for improving model capabilities in increasingly complex recognition tasks in the future. Tang's network structure 71.2 [37] Caffe-imageNet 65.5 [22] MNF CNN+L2 SVM 70.3 [38] Raspberry Pi 65.97 [39] DenseNet 63.50 [40] VGG16 69.40 [41] Attention CNN 70.02 [42] ResNet with gate implementation 71.80 [43] VGGNet 73.28 [44] VGG progressive SpinalNet 74.39 VGG SpinalNet 74.45 [45] Mini-Xception 66.00 [46] CNN with transfer learning 72.00 [47] CNN with HOG feature 75.…”

Section: Resultsmentioning

confidence: 99%

Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

Sam'an,

Safuan,

Munsarif

2024

IJEECS

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The development of artificial intelligence in facial emotion recognition (FER) is rapidly growing and has been widely applied in various fields. Deep learning (DL) techniques with evolutionary algorithms have become the preferred choice for solving various security, health, gaming, and other related problems. This research proposes the use of a genetic algorithm (GA) as the main method to optimize hyperparameters in the convolutional neural network (CNN) model for FER. The required computation time is approximately 37 hours 57 minutes 55 seconds, with generation 3 taking the longest time at around 16 hours 45 minutes 4 seconds. However, generation 3 achieved an accuracy of 76.11%, which is the highest compared to other generations. The results indicate that the more generations are involved, the higher the achievable accuracy. Furthermore, the proposed CNN-GA model in this study outperforms previous models that have been examined. Thus, this study makes a significant contribution to improving the understanding of using GAs to optimize the performance of CNN models for FER.

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“…It is the most widely used facial expression dataset captured under controlled laboratory conditions at present [ 23 ]. In order to be compatible with the seven basic expressions in the Fer2013 dataset, contempt expressions with a small sample size were removed in this experiment, and 3 to 5 frames were intercepted from each image sequence as expression samples in this experiment [ 24 ]. Finally, the obtained data samples are randomly divided into a training set and a test set according to the ratio of 9 : 1.…”

Section: Methodsmentioning

confidence: 99%

Emotion Recognition of Online Education Learners by Convolutional Neural Networks

2022

Computational Intelligence and Neuroscience

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At present, the facial expression recognition model in video communication has problems such as weak network generalization ability and complex model structure, which leads to a large amount of computation. Firstly, the Inception architecture is adopted as a design philosophy. The Visual Geometry Group Network (VGGNet) model is improved. Multiscale kernel convolutional layers are constructed to obtain more expressive features. Secondly, the attention mechanism is integrated into a multiscale feature fusion network to form a multiattention mechanism Convolutional Neural Network (CNN) model. Novel spatial and multichannel attention models are designed. The effects of redundant information and noise are reduced. Finally, experiments are carried out on the Fer2013 dataset and the Extended Cohn-Kanade Dataset (CK+) to verify the detection accuracy of the model. The results show that the Delivered Duty Unpaid (DDU) loss can be used for facial expression recognition in complex environments. After the attention module is added, the overall recognition accuracy of the network on Fer2013 and CK+ has been improved to varying degrees. The addition of the channel attention module has a more obvious effect on the recognition accuracy compared with the spatial attention module. The addition of the attention module enables the network to increase the attention to error-prone samples. The improved network model can better extract the key features of facial expressions, enhance the feature discrimination ability, and improve the recognition accuracy of error-prone expressions. The accuracy rate of facial expression recognition with larger movements is over 98%. Facial expressions are an important way of communication between people, and online video has greatly limited this communication method. The proposed CNN model based on multiscale feature fusion will effectively solve these network limitations and have an important and positive impact on future network information exchange.

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Emotion Recognition on FER-2013 Face Images Using Fine-Tuned VGG-16

Cited by 37 publications

References 43 publications

Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning

Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning

Convolutional neural network hyperparameters for face emotion recognition using genetic algorithm

Emotion Recognition of Online Education Learners by Convolutional Neural Networks

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