Khan, MariyamMariyamKhanHussain, ShafiqShafiqHussainAmjad, AdeenAdeenAmjadJamil, AleenaAleenaJamilDr. AZHAR MuhammadUsman, MehwishMehwishUsmanAhmad, WaqarWaqarAhmadMansab, Arslan AliArslan AliMansabAkbar, Muhammad HamzaMuhammad HamzaAkbar2026-06-182026-06-182025Spectrum of Engineering Sciences, 2025, vol. 3(10), pp. 1976-1985.3007-31383007-312xhttp://hdl.handle.net/20.500.11861/27551Open accessThis 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.enNdex Terms—Facial Expression Recognition (FER)Deep LearningReal-Time ProcessingExpression Intensity EstimationConvolutional Neural Network (CNN)Spontaneous ExpressionsPeer Reviewed Journal Article