Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learning

Lasri, Imane; Riadsolh, Anouar; Elbelkacemi, Mourad

doi:10.1007/s10639-022-11370-4

Cited by 18 publications

(7 citation statements)

References 29 publications

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“…When compared to other research, the proposed models are competitive, often outperforming or matching other state-of-the-art results. The model of Dada et al 53 , Lasri et al 55 , and Baygin et al 56 stand out with slightly higher metrics on JAFFE. However, it should be noted that the proposed models are trained and tuned using the pre-processing and hyperparameters specifically tailored for Emognition dataset, while the existing models were developed and trained for JAFFE and KDEF datasets.…”

Section: Resultsmentioning

confidence: 82%

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Agung,

Rifai,

Wijayanto

2024

Sci Rep

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Detecting emotions from facial images is difficult because facial expressions can vary significantly. Previous research on using deep learning models to classify emotions from facial images has been carried out on various datasets that contain a limited range of expressions. This study expands the use of deep learning for facial emotion recognition (FER) based on Emognition dataset that includes ten target emotions: amusement, awe, enthusiasm, liking, surprise, anger, disgust, fear, sadness, and neutral. A series of data preprocessing was carried out to convert video data into images and augment the data. This study proposes Convolutional Neural Network (CNN) models built through two approaches, which are transfer learning (fine-tuned) with pre-trained models of Inception-V3 and MobileNet-V2 and building from scratch using the Taguchi method to find robust combination of hyperparameters setting. The proposed model demonstrated favorable performance over a series of experimental processes with an accuracy and an average F1-score of 96% and 0.95, respectively, on the test data.

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Section: Resultsmentioning

confidence: 82%

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Agung,

Rifai,

Wijayanto

2024

Sci Rep

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“…Additionally, it can integrate some structure, like sequences that encode the order of mentioned concepts (part-of-speech tags, as in [211]) or visual objects [152]. The guiding inputs can encapsulate the user's interest in object relationships, as exemplified through the use of verbs and semantic roles to depict activities within the image and the involvement of objects in these activities [236]- [239]. Alternatively, using scene graphs generated or selected by users can also be used [237], [238].…”

Section: ) Controllable Captioningmentioning

confidence: 99%

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential

Jamil,

Saif-Ur-Rehman,

Mahmood

et al. 2024

IEEE Access

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Generative intelligence relies heavily on the integration of vision and language. Much of the research has focused on image captioning, which involves describing images with meaningful sentences. Typically, when generating sentences that describe the visual content, a language model and a vision encoder are commonly employed. Because of the incorporation of object areas, properties, multi-modal connections, attentive techniques, and early fusion approaches like bidirectional encoder representations from transformers (BERT), these components have experienced substantial advancements over the years. This research offers a reference to the body of literature, identifies emerging trends in an area that blends computer vision as well as natural language processing in order to maximize their complementary effects, and identifies the most significant technological improvements in architectures employed for image captioning. It also discusses various problem variants and open challenges. This comparison allows for an objective assessment of different techniques, architectures, and training strategies by identifying the most significant technical innovations, and offers valuable insights into the current landscape of image captioning research.

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“…The prevailing success of deep learning has made person reidentification no-exception. The earlier approaches based on deep learning, such as [3], [16]- [18], naively applied CNN backbones to extract global features. Due to the limitation of being prone to ignore local information from small regions [19], more and more works focus on learning local features.…”

Section: A Local Feature Learning For Person Re-identificationmentioning

confidence: 99%

Hierarchical Attentive Feature Aggregation for Person Re-Identification

Dong,

2024

IEEE Access

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Facial emotion recognition of deaf and hard-of-hearing students for engagement detection using deep learning

Cited by 18 publications

References 29 publications

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Image-based facial emotion recognition using convolutional neural network on emognition dataset

Deep Learning Approaches for Image Captioning: Opportunities, Challenges and Future Potential

Hierarchical Attentive Feature Aggregation for Person Re-Identification

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