A Robust-Facial Expressions Recognition System using Deep Learning Architectures

Ilyas, Bendjillali Ridha; Abderrazak, Tadjeddine Ali; Sofiane, Bendelhoum Mohamed; Bahidja, Boukenadil; Imane, Houam; Miloud, Kamline

doi:10.1109/dasa59624.2023.10286798

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Other1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Sidhom,

Ghazouani,

Barhoumi

et al. 2024

Procedia Computer Science

View full text Add to dashboard Cite

A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Sidhom,

Ghazouani,

Barhoumi

et al. 2024

Procedia Computer Science

View full text Add to dashboard Cite

Arabic Handwriting Recognition Using Deep Learning and Simulated Annealing

Boukenadil,

Ilyas,

Sofiane

et al. 2024

2024 4th International Conference on Embedded &Amp;amp; Distributed Systems (EDiS)

View full text Add to dashboard Cite

Restoration of ancient Arabic manuscripts: a deep learning approach

Miloud,

Abdelmounaim,

Mohammed

et al. 2024

SEES

View full text Add to dashboard Cite

This paper explores the application of modern deep learning methodologies to the restoration of highly valuable ancient Arabic manuscripts, a task of immense cultural and historical importance. Our approach meticulously guides readers through the experimental process, placing a strong emphasis on crucial components such as the selection of appropriate loss functions, the architecture of hidden layers, and the optimization techniques used. The results of our research are nothing short of extraordinary, particularly with the implementation of the proposed Modified Attention-Based Bidirectional Long Short-Term Memory (M-AB-LSTM) model, which achieved an outstanding accuracy rate of 99.50%. This work transcends traditional image enhancement techniques; it plays a pivotal role in not only making fragments of our rich cultural heritage accessible but also in ensuring the preservation of these priceless and unique artifacts for future generations. Such an effort is of profound significance to humanity as a whole. Additionally, we highlight the extensive and labor-intensive process involved in manually curating finely tuned and accurately classified datasets, which includes a comprehensive collection of 3,745 ancient Arabic manuscripts.

show abstract

A Robust-Facial Expressions Recognition System using Deep Learning Architectures

Cited by 4 publications

References 23 publications

A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

A Facial Morphology-Guided Feature Selection Method For Spontaneous Expression Recognition

Arabic Handwriting Recognition Using Deep Learning and Simulated Annealing

Restoration of ancient Arabic manuscripts: a deep learning approach

Contact Info

Product

Resources

About