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Neural Computing and Applications

Quantum-based deep learning method for recognition of facial expressions

Roopa Golchha, Mridu Sahu, Vikrant Bhateja

Published Mar 13, 2025• 4 citations
Publisher
DOI
10.1007/s00521-024-10968-8

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The purpose of this work is to analyze how new technologies can enhance clinical practice while also examining the physical traits of emotional expressiveness of face expression in a number of psychiatric illnesses. Hence, in this work, an automatic facial expression recognition system has been proposed that analyzes static, sequential, or video facial images from medical healthcare data to detect emotions in people's facial regions. The proposed method has been implemented in five steps. The first step is image preprocessing, where a facial region of interest has been segmented from the input image. The second component includes a classical deep feature representation and the quantum part that involves successive sets of quantum convolutional layers followed by random quantum variational circuits for feature learning. Here, the proposed system has attained a faster training approach using the proposed quantum convolutional neural network approach that takes [Formula: see text] time. In contrast, the classical convolutional neural network models have [Formula: see text] time. Additionally, some performance improvement techniques, such as image augmentation, fine-tuning, matrix normalization, and transfer learning methods, have been applied to the recognition system. Finally, the scores due to classical and quantum deep learning models are fused to improve the performance of the proposed method. Extensive experimentation with Karolinska-directed emotional faces (KDEF), Static Facial Expressions in the Wild (SFEW 2.0), and Facial Expression Recognition 2013 (FER-2013) benchmark databases and compared with other state-of-the-art methods that show the improvement of the proposed system.
The rapid growth of online education has emphasized the importance of maintaining active student engagement to ensure effective learning outcomes.Traditional engagement detection methods use manual observation and they are time-consuming for large-scale datasets.This research presents a quantum-enhanced facial engagement recognition (Q-FER) system to overcome these constraints.This framework is inspired by the principles of quantum machine learning (QML).The suggested system merges the advantages of traditional deep learning (DL) and quantum computing (QC) by utilizing DL methods for feature extraction and employing a quantum convolutional neural network (QCNN) for classification.This approach utilizes quantum principles (QP) such as entanglement and superposition which enable the model to analyze numerous data patterns simultaneously.The dataset comprises 2.120 facial images of students captured during virtual classes.These images are classified into six emotional states, which are again categorized into two engagement levels: engaged and not engaged.The model employs Z-score normalization along with data augmentation methods to enhance generalization and minimize sensitivity to changes in lighting, orientation and facial expressions.Experimental results show that the Q-FER achieves a classification accuracy of 99.76%, significantly outperforming traditional convolutional neural networks (CNN).These results underscore the promise of quantum-assisted deep learning systems for analyzing real-time engagement in e-learning.
Emotion recognition seeks to identify a person’s state by examining signals such as facial expressions, speech, physiological signals and conversational content. Yet accurate emotion recognition presents obstacles because these signals can be complex, noisy, nonlinear and heavily affected by surrounding context. This paper investigates quantum-based representations combined with machine learning techniques to enhance emotion classification. The research examines emotion sources, including facial expressions, speech, EEG signals and conversational data. It explores how quantum-inspired techniques can merge with established deep learning methods to produce representations. The design presented fuses a neural network with a four qubit ZZFeatureMap component enabling the system to learn data from both conventional and quantum derived feature spaces. Experiments examined EEG segments for each participant and the system achieved average recognition rates of seventy nine point six percent for valence and eighty one point five percent for arousal across ten repeated trials. These findings show that quantum derived features can add information when combined with conventional deep learning methods. Nevertheless because experiments used simulated circuits the results do not prove a performance advantage over conventional techniques on actual quantum computers. The study also highlights difficulties such as quantum noise, computational scalability, experimental assessment, data privacy and real world deployment.