Facial expression recognition via transfer learning

Li, Bin

doi:10.4108/eai.8-4-2021.169180

Cited by 4 publications

(4 citation statements)

References 11 publications

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“…The approach utilizes pre-trained convolutional neural networks, including MobileNet, Inception V3, ResNet50, and VGG19, all initially trained on the ImageNet database, for the task of facial emotion recognition. Li et al [37] introduced an enhanced facial emotion recognition system. They employ a multi-layer neural network trained through transfer learning, utilizing the ResNet-101 deep network for effective feature extraction.…”

Section: Transfer Learning Methodsmentioning

confidence: 99%

Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM

Chouhayebi,

Mahraz,

Riffi

et al. 2024

Computers

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Human emotion recognition is crucial in various technological domains, reflecting our growing reliance on technology. Facial expressions play a vital role in conveying and preserving human emotions. While deep learning has been successful in recognizing emotions in video sequences, it struggles to effectively model spatio-temporal interactions and identify salient features, limiting its accuracy. This research paper proposed an innovative algorithm for facial expression recognition which combined a deep learning algorithm and dynamic texture methods. In the initial phase of this study, facial features were extracted using the Visual-Geometry-Group (VGG19) model and input into Long-Short-Term-Memory (LSTM) cells to capture spatio-temporal information. Additionally, the HOG-HOF descriptor was utilized to extract dynamic features from video sequences, capturing changes in facial appearance over time. Combining these models using the Multimodal-Compact-Bilinear (MCB) model resulted in an effective descriptor vector. This vector was then classified using a Support Vector Machine (SVM) classifier, chosen for its simpler interpretability compared to deep learning models. This choice facilitates better understanding of the decision-making process behind emotion classification. In the experimental phase, the fusion method outperformed existing state-of-the-art methods on the eNTERFACE05 database, with an improvement margin of approximately 1%. In summary, the proposed approach exhibited superior accuracy and robust detection capabilities.

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Section: Transfer Learning Methodsmentioning

confidence: 99%

Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM

Chouhayebi,

Mahraz,

Riffi

et al. 2024

Computers

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“…During fine-tuning, the weights of the network are adjusted to optimize its performance on the emotion recognition task. This allows the model to learn relevant facial features, such as the arrangement of facial landmarks, the shape of the mouth, or the intensity of eye expressions, that are indicative of different emotional states [29].…”

Section: Transfer Learning In Fermentioning

confidence: 99%

An In-Depth Review of Deep Facial Expression Recognition: Obstacles, Utilizations, and Prospective Directions

Khaleel Almsari,

Hussein Miry,

M. Salman

2023

IRJIET

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Facial expression recognition (FER) is emerging as an emerging and multifaceted field of study. The use of FER in areas such as healthcare, security, and safe driving has not only enhanced the credibility of these technologies, but also their integration into human computer interaction to achieve intelligent outcomes. Computational FER seeks to replicate the skill of humans in decoding facial expressions, providing important cues that complement spoken language and aid listeners in understanding. Likewise, FER's deep learning (DL) and Artificial Intelligence (AI) methodologies are meticulously designed, incorporating advanced modules for efficiency and real time processing. In light of this background, many investigations have looked at different aspects of FER. Although current surveys focus primarily on traditional technologies and generic methodologies for on premises servers, they overlook the large field of deep learning inspired by edge vision and AI assisted FER technologies.To fill this gap, the current study conducts a comprehensive and thorough analysis of the prevailing FER literature. It carefully surveys the operational framework of FER technologies, highlighting their basic and intermediate phases, as well as the underlying pattern structures. Furthermore, the study addresses the limitations inherent in current FER surveys. The exploration extends to the FER datasets, subjecting them to thorough examination, thus revealing the attendant challenges and pitfalls. In addition, it provides a comprehensive discussion of the various metrics used to measure the effectiveness of FER methods.

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“…In this layer, the spatial resolution of feature maps is reduced, which serve dimensionality reduction by [52]. After obtaining features via the convolutional layer, the next step is to integrate and classify these features [49]. If the classifier is given all of the features collected using convolution as input, it will have to do a lot of work.…”

Section: • Pooling Layersmentioning

confidence: 99%

“…Pooling operations are common in convolutional neural networks [50], and the pooling layer is frequently placed behind the convolutional layer. By pooling, the convolutional layer's output feature vectors may be lowered, and the calculation quantity can be reduced while the results are enhanced, making overfitting less likely [49]. Because images are "static," it is easy to achieve this by lowering their dimensions.…”

Section: • Pooling Layersmentioning

confidence: 99%

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

Gambo¹,

Wajiga²,

Shuib³

et al. 2018

ICST Transactions on Scalable Information Systems

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Improving the accuracy of learning style detection models is a primary concern in the area of automatic detection of learning style, which can be achieved either through, attribute/feature selection or classification algorithm. However, the role of facial expression in improving accuracy has not been fully explored in the research domain. On the other hand, deep learning solutions have become a new approach for solving complex problems using Deep Neural networks (DNNs); these DNNs have deep architectures that are capable of decomposing problems into multiple processing layers, enabling and devising multiple mapping of complex problems functions. In this paper, we investigate and compare the performance of Convolutional Neural Network (CNN) and MultiClass Neural Network (MCNN) for classification of learners into VARK learning-style dimensions (i.e Visual, Aural, Reading Kinaesthetic, including Neutral class) based on facial images. The performances of the two networks were evaluated and compared using square mean error MSE for training and accuracy metric for testing. The results show that MCNN offers better and robust classification performance of VARK learning style based on facial images. Finally, this paper has demonstrated a potential of a new method for automatic classification of VARK LS based on Facial Expressions (FEs). Based on the experimental results of the models, this approach can benefit both researchers and users of adaptive e-learning systems to uncover the potential of using FEs as identifier learning styles for recommendations and personalization of learning environments.

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Facial expression recognition via transfer learning

Cited by 4 publications

References 11 publications

Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM

Human Emotion Recognition Based on Spatio-Temporal Facial Features Using HOG-HOF and VGG-LSTM

An In-Depth Review of Deep Facial Expression Recognition: Obstacles, Utilizations, and Prospective Directions

Performance Comparison of Convolutional and Multiclass Neural Network for Learning Style Detection from Facial Images

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