Facial expression recognition using bidirectional LSTM - CNN

Febrian, Rio; Halim, Benedic Matthew; Ramdhan, Dimas; Chowanda, Andry

doi:10.1016/j.procs.2022.12.109

Cited by 27 publications

(13 citation statements)

References 11 publications

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“…In this process, an attention distribution is used to determine the weights assigned to each hidden state generated by the model. These weights are then used to calculate the weighted average result of the hidden states, which re ects the most relevant or important information in the processed image or data and improves the classi cation process of the model [59].…”

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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“…Researchers continue to explore various approaches and technologies to improve the accuracy and applicability of emotion recognition systems. Deep learning models, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and more recently, Transformer-based models, have shown significant promise in emotion recognition tasks (Kahou et al, 2015;Fan et al, 2016;John and Kawanishi, 2022;Febrian et al, 2023). Transfer learning, in which pre-trained models are fine-tuned for emotion recognition tasks, has gained popularity due to its ability to leverage large-scale labeled datasets (Feng and Chaspari, 2020).…”

Section: Emotion Recognitionmentioning

confidence: 99%

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Wang,

Yang

2024

Front. Comput. Sci.

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Children diagnosed with Autism Spectrum Disorder (ASD) often struggle to grasp social conventions and promptly recognize others' emotions. Recent advancements in the application of deep learning (DL) to emotion recognition are solidifying the role of AI-powered assistive technology in supporting autistic children. However, the cost of collecting and annotating large-scale high-quality human emotion data and the phenomenon of unbalanced performance on different modalities of data challenge DL-based emotion recognition. In response to these challenges, this paper explores transfer learning, wherein large pre-trained models like Contrastive Language-Image Pre-training (CLIP) and wav2vec 2.0 are fine-tuned to improve audio- and video-based emotion recognition with text- based guidance. In this work, we propose the EmoAsst framework, which includes a visual fusion module and emotion prompt fine-tuning for CLIP, in addition to leveraging CLIP's text encoder and supervised contrastive learning for audio-based emotion recognition on the wav2vec 2.0 model. In addition, a joint few-shot emotion classifier enhances the accuracy and offers great adaptability for real-world applications. The evaluation results on the MELD dataset highlight the outstanding performance of our methods, surpassing the majority of existing video and audio-based approaches. Notably, our research demonstrates the promising potential of the proposed text-based guidance techniques for improving video and audio-based Emotion Recognition and Classification (ERC).

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“…Secondary, the correlation was objectively analyzed by detection and facial action. Febrian et al [24] introduce the DL infrastructure for improving the efficiency of methods. Furthermore, the authors present the BiLSTM-CNN approach that integrates the presented CNN and Bi-LSTM approaches.…”

Section: Related Workmentioning

confidence: 99%

Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface

et al. 2023

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Automated facial emotion recognition (FER) is one of the important fields of human-computer interaction (HCI). FER remains challenging due to facial accessories, non-uniform illumination, pose variation, etc. Emotion detection exploiting conventional algorithms has the demerit of mutual optimization of classification and feature extraction. Artificial intelligence (AI) techniques can be employed to identify FER automatically. Deep learning (DL) driven FER models have recently allowed for designing an endto-end learning process. Therefore, this study designs a Henry Gas Solubility Optimization with Deep Learning Based FER (HGSO-DLFER) technique for HCI. The HGSO-DLFER technique aims to recognize and identify various kinds of facial emotions. To accomplish this, the HGSO-DLFER technique employs adaptive fuzzy filtering (AFF) for noise removal. In addition, the MobileNet model is used for feature vector generation, and the HGSO algorithm optimally chooses its hyperparameter scan. For the recognition of facial emotions, the HGSO-DLFER technique uses an autoencoder (AE) classifier with a Nadam optimizer. A widespread experimental analysis is made to facilitate a better understanding of the FER results by the HGSO-DLFER technique. The comparative analysis showed the effective performance of the HGSO-DLFER technique over other FER techniques with maximum accuracy of 98.65%.

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Facial expression recognition using bidirectional LSTM - CNN

Cited by 27 publications

References 11 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

EmoAsst: emotion recognition assistant via text-guided transfer learning on pre-trained visual and acoustic models

Henry Gas Solubility Optimization With Deep Learning Based Facial Emotion Recognition for Human Computer Interface

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