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Enhancing human-centric interaction: A deep learning approach for robust facial expression recognition and intensity estimation
Date Issued
2025
Journal
ISSN
3007-3138
3007-312x
Citation
Spectrum of Engineering Sciences, 2025, vol. 3(10), pp. 1976-1985.
Description
Open access
Type
Peer Reviewed Journal Article
Abstract
This paper presents a deep learning framework for real-time facial expression recognition and expression in-tensity estimation using spontaneous video data. Our system effectively tackles the most important real-world challenges like subtle variations of expressions, intersubject variability, and variations of head pose or illumination using a lightweight convolutional neural network architecture. We achieve 68.35% classification accuracy on spontaneous happy/neutral expressions with a supporting dataset of 1,442 frames annotated on a 7-point intensity scale (Cohen’s = 0.81) and show that the network can process 720p video on embedded hardware at 28 fps. The key contributions are as follows: (1) an optimized CNN model for joint classification and regression; (2) a novel postprocess-ing pipeline for context-aware intensity smoothing; and (3) an ethically collected dataset emphasizing spontaneous dynamics. Our results outperform existing baselines of spontaneous FER by 3.35%, underlining the potential of this lightweight approach in healthcare, HCI, and automotive safety.
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