AI Recruitment System Using EEG to Explore the Truth of Interviewers

Lee, Jung‐Hee; Tung, Phuong Huy; Im, Eun-Tack; Gim, Gwang-Yong

doi:10.1007/978-3-031-16485-9_6

Cited by 1 publication

(1 citation statement)

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“…Consequently, there is a critical need to develop automated systems that can accurately detect facial expressions. This need is particularly pressing in light of advancements in non-face-to-face interview techniques leveraging artificial intelligence [12], as well as the utilization of computerassisted interventions for mood disorders [13]. Hence, the demand for precise techniques for gradually recognizing facial expressions is steadily increasing.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Optimizing ANN-Based Lyapunov Stability for Facial Expression Recognition as A Base Monitoring Neurological Disorders

Ilham,

Hadiyanto,

Widodo

2023

IJCDS

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This study emphasizes the importance of facial expression recognition in identifying neurological problems in individuals with limited verbal communication abilities. Current evaluation models are time-consuming and expensive, hindering medical professionals. To address these limitations, we present an improved artificial neural network based on lyapunov stability theory (ANN-LST). By combining these models, we tackle convergence issues while encountering overfitting problems with high-dimensional data, thereby affecting prediction and analysis. Our approach employs principal component analysis (PCA) for feature reduction and extraction to effectively solve overfitting problems. The proposed model was evaluated using the Japanese female facial expression (JAFFE) database and the ahmad ilham simple face database (AIsFD) as our own database, with accuracy (ACC) as the evaluation metric. The results demonstrate higher recognition rates and faster training speeds owing to the adaptive learning rate parameters and the extraction of relevant feature information. The proposed system achieved a 13% higher success rate than face recognition systems using raw images alone. Overall, this model represents a significant advancement and offers promising applications for facial expression recognition in patients with neurological disorders.

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Section: Introduction and Related Workmentioning

confidence: 99%