CNN Learning Strategy for Recognizing Facial Expressions

Lee, Donghwan; Yoo, Ji-Sang

doi:10.1109/access.2023.3294099

Cited by 11 publications

(6 citation statements)

References 38 publications

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“…Table IV shows the frequent use of deep learning algorithms in emotional recognition in research. [19], [20], [21], [22], [5], [23], [24], [25], [26], [27], [28], [5], [4], [29], [30], [31], [32], [33], [34], [35], [36], [37], [38] CNN + GoogleNet [39], [40], [41], [42] CNN + VGG 19 [43], [44], [27], [28], [5], [42] CNN + VGG There is also SHCNN which uses Leaky ReLU to avoid the "Dead ReLU problem" which can bring better convergence on the dataset [65]. CNN can also combine with CRBM and Transfer Learning.…”

Section: Resultsmentioning

confidence: 99%

Systematic Literature Review: The Influence and Effectiveness of Deep Learning in Image Processing for Emotion Recognition

Wicaksana,

Davinsi,

Afriyanto

et al. 2024

Preprint

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In the current digital era, image processing and Emotion Recognition are important topics in the field of artificial intelligence. Deep learning, as one of the most widely used AI techniques in pattern recognition, has shown great potential in addressing these challenges. This research employs a Systematic Literature Review method to collect and analyze previous studies related to deep learning algorithms, namely Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), allowing the researchers to conclude efficient deep learning methods for emotion recognition through image processing. This paper has the result that most studies used CNN to identify emotion from facial expressions, while some studies used RNN. Furthermore, some researchers used combined CNN and RNN to identify emotion from images. Based on the analysis of this research, it is recommended that further studies to take a more holistic approach by considering a wider range of indicators that can be used as signs or signals to analyze a person's emotions. This approach allows for a comprehensive understanding of emotions from multiple perspectives.

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Section: Resultsmentioning

confidence: 99%

Systematic Literature Review: The Influence and Effectiveness of Deep Learning in Image Processing for Emotion Recognition

Wicaksana,

Davinsi,

Afriyanto

et al. 2024

Preprint

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“…There are also five convolution layers, three fully connected layers, two average pooling layers, and one max pooling layer. The input size for this model is 48 × 48 [48,49].…”

Section: Facial Expression-based Emotion Recognitionmentioning

confidence: 99%

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

Roshdy,

Karar,

Kork

et al. 2024

Applied Sciences

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This paper addresses the limitations of relying solely on facial expressions for emotion recognition by proposing an advanced approach that emphasizes continuous monitoring of electroencephalography (EEG) signals. Recognizing the potential for deception in facial expressions, our study leverages the growing interest in EEG signals, tapping into advancements in deep learning and machine learning. By optimizing the configuration of EEG electrodes, our approach enhances the accuracy of emotion classification systems, offering a streamlined solution. The proposed multi-input system refines EEG-based emotion recognition efficiency and integrates facial expression analysis to enhance overall system effectiveness. Through the application of brain heat map topographies and facial expression recognition, our system, employing just nine electrodes, outperforms basic emotion recognition setups. Experimental results validate that combining facial expression analysis with EEG signals provides a more comprehensive and accurate understanding of human emotions. This innovative approach holds significance across various sectors, including healthcare, psychology, and human–computer interaction. The paper introduces a novel multi-input system approach, collaboratively fusing two powerful deep learning algorithms: two Convolutional Neural Networks (CNNs). The proposed EEG-based CNN algorithm achieves an efficiency of 87.43%, rising to 91.21% when integrated with the DeepFace CNN. The seamless integration of facial expressions and brain topographies enables the system to efficiently harness abundant information from both modalities, ensuring a thorough comprehension of human emotions. By capitalizing on the combined advantages of analyzing facial expressions and EEG-derived brain topography, this avant-garde technique substantially improves both precision and efficiency in emotion recognition systems. This enhancement establishes a foundation for the introduction of innovative applications across a spectrum of fields.

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“…Lee and Yoo. [24] Focuses on enhancing facial expression recognition (FER) performance in computer vision, acknowledging the challenges posed by varying degrees of similarity among different expressions. The authors propose a novel divideand-conquer learning strategy, utilizing MobileNet for face detection and ResNet-18 as the backbone deep neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The novel divide-and-conquer learning strategy, utilizing MobileNet and ResNet-18, showcases significant improvements in facial expression recognition accuracy across thermal and RGB datasets. The emphasis on integrated architectures and the potential extension to Vision Transformer-based models reflects a comprehensive approach [24].…”

Section: B Cnn Learning Strategy For Recognizing Facial Expressionsmentioning

confidence: 99%

Facial Expression Recognition Based on Deep Learning: A Review

Jajan,

Abdulazeez

2024

ijcs

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This review paper provides a comprehensive analysis of recent advancements in Facial Expression Recognition (FER) through various deep learning models. Seven state-of-the-art models are scrutinized, each offering unique contributions to the field. The MBCC-CNN model demonstrates improved recognition rates on diverse datasets, addressing the challenges of facial expression recognition through multiple branches and cross-connected convolutional neural networks. The Deep Graph Fusion model introduces a novel approach for predicting viewer expressions from videos, showcasing superior performance on the EEV database. Multimodal emotion recognition is explored in the EEG and facial expression fusion model, achieving high accuracy on the DEAP dataset. The Spark-based LDSP-TOP descriptor, coupled with a 1-D CNN and LSTM Autoencoder, excels in capturing temporal dynamics for facial expression understanding. Vision transformers for micro-expression recognition exhibit outstanding accuracy on datasets like CASMEI, CASME-II, and SAMM. Additionally, a hierarchical deep learning model is proposed for evaluating teaching states based on facial expressions. Lastly, a visionary transformer model achieves remarkable recognition accuracy of 100% on SAMM dataset, showcasing the potential of combining convolutional and transformer architectures. This review synthesizes key findings, highlights model performances, and outlines directions for future research in FER.

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CNN Learning Strategy for Recognizing Facial Expressions

Cited by 11 publications

References 38 publications

Systematic Literature Review: The Influence and Effectiveness of Deep Learning in Image Processing for Emotion Recognition

Systematic Literature Review: The Influence and Effectiveness of Deep Learning in Image Processing for Emotion Recognition

Advancements in EEG Emotion Recognition: Leveraging Multi-Modal Database Integration

Facial Expression Recognition Based on Deep Learning: A Review

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