Facial Emotion Recognition Using Transfer Learning of AlexNet

Sekaran, Sarmela Raja; Lee, Chin Poo; Lim, Kian Ming

doi:10.1109/icoict52021.2021.9527512

Cited by 34 publications

(14 citation statements)

References 29 publications

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“…These limitations are their manual design, less characteristic information, and other problems. [17], VGGNet [18], Googlenet [19], ResNet [20], MobileNet [21], and DensNet [22]. Naim et al [37] merged a CNN and SVM to produce a new model that improved the recognition rate of facial expressions.…”

Section: Facial Expression Recognitionmentioning

confidence: 99%

“…Ruan et al [16] proposed a novel deep disturbance-disentangled learning (DDL) method for FER. To improve the recognition rate of these facial expressions, CNN models continuously add depth and breadth to the network [17][18][19][20][21][22]. However, with an increase in the depth and width of the network, the number of parameters will also increase rapidly.…”

Section: Introductionmentioning

confidence: 99%

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The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Han

2023

Symmetry

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Due to the small data and unbalanced sample distribution in the existing facial emotion datasets, the effect of facial expression recognition is not ideal. Traditional data augmentation methods include image angle modification, image shearing, and image scrambling. The above approaches cannot solve the problem that is the high similarity of the generated images. StarGAN V2 can generate different styles of images across multiple domains. Nevertheless, there are some defects in gener-ating these facial expression images, such as crooked mouths and fuzzy facial expression images. To service such problems, we improved StarGAN V2 by solving the drawbacks of creating pictures that apply an SENet to the generator of StarGAN V2. The generator’s SENet can concentrate at-tention on the important regions of the facial expression images. Thus, this makes the generated symmetrical expression image more obvious and easier to distinguish. Meanwhile, to further im-prove the quality of the generated pictures, we customized the hinge loss function to reconstruct the loss functions that increase the boundary of real and fake images. The created facial expression pictures testified that our improved model could solve the defects in the images created by the original StarGAN V2. The experiments were conducted on the CK+ and MMI datasets. The correct recognition rate of the facial expressions on the CK+ was 99.2031%, which is a 1.4186% higher accuracy than that of StarGAN V2. The correct recognition rate of the facial expressions on the MMI displays was 98.1378%, which is 5.059% higher than that of the StarGAN V2 method. Furthermore, contrast test outcomes proved that the improved StarGAN V2 performed better than most state-of-the-art methods.

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Section: Facial Expression Recognitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Han

2023

Symmetry

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“…A technique of transfer learning is to transfer the pretrained model to a new model for task training. For example, Sekaran et al [15] used the AlexNet model, which was pretrained as ImageNet, for FER transfer learning. The source domain was ImageNet (the source task was object recognition, and the target domains are FER2013 and CK+ (the target task was emotion classification).…”

Section: Related Workmentioning

confidence: 99%

Discussions of Different Deep Transfer Learning Models for Emotion Recognitions

Yen

2022

IEEE Access

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In recent years, facial emotion recognition (FER) has been a popular topic in affective computing. However, FER still faces many challenges in automatic recognition for several reasons, including quality control of sample data, extraction of effective features, creation of models, and multi-feature fusion, which have not been thoroughly researched and therefore are still hot topics in computer visualization. In view of the mature development of deep learning, deep learning methods are increasingly being used in FER. However, because deep learning requires a large amount of data to achieve effective training, many studies have employed transfer learning to compensate for this drawback. Nevertheless, there has been no universal approach for transfer learning in FER. Accordingly, this study used the five classic models in FER (i.e., ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet121) to conduct a series of experiments: data preprocessing, training type, and the applicability of multi-stage pretraining. According to the results, class wight was the optimal technique for data balance. In addition, the freeze + fine-tuning training type can produce higher accuracy, regardless of the size of the dataset. Multi-stage training was also effective. Compared with the model accuracy in previous studies, the accuracy achieved in this study using the proposed transfer learning method was superior for both large and small datasets. Specifically, on AffectNet, the accuracy for the ResNet-50, Xception, EfficientNet-B0, Inception, and DenseNet-121 models increased by 8.37%, 10.45%, 10.45%, 8.55%, and 5.47%, respectively. On FER2013, the accuracy for these models increased by 5.72%, 2%, 10.45%, 5%, and 9%, respectively. These results proved the validity and advantages of the experiments in this study.

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“…This section compares the results of this paper's algorithm with those of other algorithms for analysis. Other algorithms include AlexNet in the literature [37], Inception-v3 in the literature [38], ResNet-50 in the literature [23], DenseNet-201 in the literature [39], and DSK-Net in the literature [40]. All of the above network models add the SMP module before the fully connected layer so that the network model is not constrained by the image size.…”

Section: Attnest Compared With Other Algorithmsmentioning

confidence: 99%

Mathematical Formula Image Screening Based on Feature Correlation Enhancement

et al. 2022

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There are mathematical formula images or other images in scientific and technical documents or on web pages, and mathematical formula images are classified as either containing only mathematical formulas or formulas interspersed with other elements, such as text and coordinate diagrams. To screen and collect images containing mathematical formulas for others to study or for further research, a model for screening images of mathematical formulas based on feature correlation enhancement is proposed. First, the Feature Correlation Enhancement (FCE) module was designed to improve the correlation degree of mathematical formula features and weaken other features. Then, the strip multi-scale pooling (SMP) module was designed to solve the problem of non-uniform image size, while enhancing the focus on horizontal formula features. Finally, the loss function was improved to balance the dataset. The accuracy of the experiment was 89.50%, which outperformed the existing model. Using the model to screen images enables the user to screen out images containing mathematical formulas. The screening of images containing mathematical formulas helps to speed up the creation of a database of mathematical formula images.

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Facial Emotion Recognition Using Transfer Learning of AlexNet

Cited by 34 publications

References 29 publications

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

The Facial Expression Data Enhancement Method Induced by Improved StarGAN V2

Discussions of Different Deep Transfer Learning Models for Emotion Recognitions

Mathematical Formula Image Screening Based on Feature Correlation Enhancement

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