Mobile-based biology edutainment application for secondary schools

Al-Modwahi, Ashraf Abbas M.; Kaisara, Onalenna; Parkizkar, Behrang; Lashkari, Arash Habibi

doi:10.1117/12.2011320

Cited by 1 publication

(1 citation statement)

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“…In the field of human-computer interaction, detecting neutral expressions can be useful for developing more natural and efficient ways for people to communicate with computers. In security contexts, such as border control or law enforcement, accurately detecting neutral expressions can be important for identifying individuals who may be attempting to conceal their emotions or intentions [8].…”

Section: Of 26mentioning

confidence: 99%

A Data-Centric Approach for Detecting a Neutral Facial Expression using Deep Learning

Sousa,

Canedo,

Drummond

et al. 2023

Preprint

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Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.

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Section: Of 26mentioning

confidence: 99%