Maha Albalhaq
Papers
1
Total Citations
12
H-Index
1
About
Maha Albalhaq is a researcher advancing the field of affective computing and human-computer interaction, with a primary focus on facial expression recognition using deep learning. Her most-cited work, "Impact of Activation, Optimization, and Regularization Methods on the Facial Expression Model Using CNN" (2022, 12 citations), systematically investigates how architectural choices in convolutional neural networks influence the accurate detection of emotional states from facial cues. This contribution is critical for enabling more natural and intuitive interfaces in applications ranging from data-driven animation to human-robot collaboration. By dissecting the effects of various activation functions, optimization algorithms, and regularization techniques, Albalhaq provides a practical roadmap for building robust emotion recognition models. Her research addresses a fundamental challenge in human-computer interaction: the need for machines to reliably interpret non-verbal communication. Through this work, she has laid important groundwork for systems that can better understand and respond to human emotional expressions, making her a notable voice in the ongoing effort to bridge the gap between human sentiment and machine perception.
Research Focus
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Top Papers
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