Optimized efficient attention-based network for facial expressions analysis in neurological health care

Munsif, Muhammad; Sajjad, Muhammad; Ullah, Mohib; Tarekegn, Adane Nega; Cheikh, Faouzi Alaya; Tsakanikas, Panagiotis; Muhammad, Khan

doi:10.1016/j.compbiomed.2024.108822

Computers in Biology and Medicine

2024

DOI: 10.1016/j.compbiomed.2024.108822

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

Muhammad Munsif,

Muhammad Sajjad,

Mohib Ullah

et al.

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Cited by 3 publications

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Computer Communications

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A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks

Ali,

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Jiang

et al. 2025

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Khan,

Usman,

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Image and Vision Computing

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A New Joint Training Method for Facial Expression Recognition with Inconsistently Annotated and Imbalanced Data

Chen,

Zhang,

Lee

2024

Electronics

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Facial expression recognition (FER) plays a crucial role in various applications, including human–computer interaction and affective computing. However, the joint training of an FER network with multiple datasets is a promising strategy to enhance its performance. Nevertheless, widespread annotation inconsistencies and class imbalances among FER datasets pose significant challenges to this approach. This paper proposes a new multi-dataset joint training method, Sample Selection and Paired Augmentation Joint Training (SSPA-JT), to address these challenges. SSPA-JT models annotation inconsistency as a label noise problem and selects clean samples from auxiliary datasets to expand the overall dataset size while maintaining consistent annotation standards. Additionally, a dynamic matching algorithm is developed to pair clean samples of the tail class with noisy samples, which enriches the tail classes with diverse background information. Experimental results demonstrate that SSPA-JT achieved superior or comparable performance compared with the existing methods by addressing both annotation inconsistencies and class imbalance during multi-dataset joint training. It achieved state-of-the-art performance on RAF-DB and CAER-S datasets with accuracies of 92.44% and 98.22%, respectively, reflecting improvements of 0.2% and 3.65% over existing methods.

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Optimized efficient attention-based network for facial expressions analysis in neurological health care

Cited by 3 publications

References 43 publications

A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks

A deep dive into cybersecurity solutions for AI-driven IoT-enabled smart cities in advanced communication networks

Attention enhanced machine instinctive vision with human-inspired saliency detection

A New Joint Training Method for Facial Expression Recognition with Inconsistently Annotated and Imbalanced Data

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