Abstract Facial expressions bring human interactions to life with nonverbal cues that convey hues of emotions, feelings, and cultural intent. Analyzing these expressions is essential in the age of digital transformation. Traditional approaches to real-time facial expression analysis have limitations in capturing the complexity of these expressions and consume enormous computational power. The promising computing capacity of quantum computers is poised to solve current problems and set new standards by meeting growing demands. We have created a hybrid model that leverages the capabilities of the classical and quantum domains. The proposed model uses a quantum distance-based classifier along with classical artificial neural networks to perform real-time facial expression analysis and classification. Based on a thorough review of the literature, this novel and comprehensive work is, to the best of our knowledge, one of the few that addresses eight different emotions in the quantum domain. The implementation of a novel quantum error correction method has improved the accuracy of this hybrid model. Our model was trained on the CK+ facial expression database. To ensure fairness of the study, we tested our model on the largest facial expression database, AffectNet-8, and compared the performance with state-of-the-art models. Our model accuracy is 10.83% higher than that of the state-of-the-art models. From a holistic perspective, our proposed novel hybrid model appears to have universal value for all kinds of real-time image analysis and classification problems. As we move forward, we planned to focus on quantum neural networks for image processing and facial expression analysis.
Abstract Facial expressions bring human interactions to life with nonverbal cues that convey hues of emotions, feelings, and cultural intent. Analyzing these expressions is essential in the age of digital transformation. Traditional approaches to real-time facial expression analysis have limitations in capturing the complexity of these expressions and consume enormous computational power. The promising computing capacity of quantum computers is poised to solve current problems and set new standards by meeting growing demands. We have created a hybrid model that leverages the capabilities of the classical and quantum domains. The proposed model uses a quantum distance-based classifier along with classical artificial neural networks to perform real-time facial expression analysis and classification. Based on a thorough review of the literature, this novel and comprehensive work is, to the best of our knowledge, one of the few that addresses eight different emotions in the quantum domain. The implementation of a novel quantum error correction method has improved the accuracy of this hybrid model. Our model was trained on the CK+ facial expression database. To ensure fairness of the study, we tested our model on the largest facial expression database, AffectNet-8, and compared the performance with state-of-the-art models. Our model accuracy is 10.83% higher than that of the state-of-the-art models. From a holistic perspective, our proposed novel hybrid model appears to have universal value for all kinds of real-time image analysis and classification problems. As we move forward, we planned to focus on quantum neural networks for image processing and facial expression analysis.
Rapid advancements in image and video processing technologies are poised to create remarkable impacts on a wide range of industries. A significant challenge in these processing technologies resides in identifying the features fed for image classification algorithms. Though all classification algorithms could identify, extract and classify the features of a given image, their accuracy is directly proportional to the number of sample points taken from the image using a sampling technique. As the accuracy improves with a substantial number of sample points, the time consumed to process them looms large. These challenges beseech enormous computing power. Quantum computers avowed exceptional computing power is expected to bridge the growing demands. To address these challenges effectively, we have chosen a specific problem, Facial Expression Analysis, to explore in-depth and arrive at a purposeful approach to deliver the desired outcome. The purpose of this paper is two-pronged. Perform a comparative study of accuracy and performance of classical and quantum image processing algorithms in classical and quantum computers, respectively. Secondly, devise a novel hybrid model using a quantum distance-based classifier augmented with a classical linear support vector machine to overcome the limitations observed. Sample image features derived from the quantum classifier were used to train the linear classifier. The results were observed to be better relative to results from the classical distance-based classifier. Holistically, the novel hybrid model is observed as a promising solution for all image classification problems. Our future work will focus on sophisticated usage of a linear classification algorithm in quantum computing.
Facial expression recognition (FER) underpins applications in affective computing but remains challenged by computational cost and the ambiguity of compound emotions. We introduce a Hybrid-Quantum Convolutional Neural Network (HQ-CNN) that integrates quantum principles (superposition, entanglement) into a classical CNN pipeline to enhance representational power and efficiency. Evaluated on the Real-World Affective Faces Database (RAF-DB), the HQ-CNN improves accuracy by 4.60% on basic emotions and 4.47% on compound emotions, while reducing computation time by up to 22.11% and 6.20%, respectively, relative to a VGG16 baseline. Confusion-matrix analysis shows fewer misclassifications on challenging compound categories, indicating better separation of overlapping affective cues. These results support the use of quantum-enhanced architectures as a viable path toward robust, real-time FER systems.
The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. This study explores the potential of quantum computing in addressing these challenges through the development of a Deep Quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58
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.
This Facial emotion recognition plays a pivotal role in human-computer interaction, with far-reaching implications spanning psychology, healthcare, and human computer interaction. Conventional approaches, primarily utilizing classical machine learning methods, frequently face challenges in effectively interpreting intricate facial expressions under diverse environmental circumstances. However, the burgeoning paradigm of quantum machine learning (QML) presents a transformative approach, leveraging the principles of quantum computing to potentially revolutionize the landscape of emotion detection from facial expressions. This abstract explores the convergence of quantum computing and facial emotion recognition, proposing a novel framework wherein facial features undergo encoding into quantum states, undergo processing through quantum circuits, and ultimately translate into predictions of emotional states. Within this context, quantum algorithms like variational algorithms and quantum neural networks emerge as promising avenues for both training and inference tasks, presenting unparalleled opportunities to improve the accuracy and efficiency of emotion recognition systems. Despite the immense potential offered by QML, practical implementation encounters formidable challenges, including noise, decoherence, and scalability constraints inherent in existing quantum devices. However, ongoing research endeavors persist in addressing these obstacles, driven by the promise of transforming emotion recognition systems into more nuanced and robust entities. As the integration of quantum computing principles advances, it holds the promise of facilitating deeper insights into human emotions, thereby fostering more seamless and intuitive human-computer interaction experiences. By bridging the gap between quantum computing and facial emotion recognition, this abstract underscores the transformative potential of QML in reshaping the future of emotion recognition systems, paving the way for more empathetic and responsive technological interfaces.
Micro-expression recognition has emerged as a critical area of study in affective computing, with significant applications in psychological analysis, security, and human-computer interaction. However, traditional methods often struggle to accurately capture and interpret subtle, rapid, and gender-specific facial micro-expressions due to challenges such as noise, limited datasets, and variations in expression intensity. This paper introduces an innovative approach, the Error-Corrected Quantum Convolutional Neural Network (EQCNN), to address these challenges and enhance the recognition of gender-based micro-expressions. The proposed EQCNN leverages the principles of quantum computing, incorporating error detection and correction mechanisms within its architecture. By employing quantum unitary gates, Hadamard gates, and CNOT gates, the model ensures data integrity and mitigates the effects of quantum decoherence. Additionally, the framework utilizes a 3π/2 phase shift and logical qubits to correct errors dynamically, optimizing the representation of micro-expression features. To validate the effectiveness of EQCNN, the model was evaluated on a gender-annotated micro-expression dataset. The results demonstrate outstanding performance, achieving an accuracy of 99.3%, an F1-score of 98.9%, a precision of 96.7%, and a recall of 98.4%. Comparative analyses show that EQCNN significantly outperforms classical and quantum-enhanced models, particularly in capturing subtle gender-specific nuances in facial expressions. The findings of this study not only advance the field of micro-expression recognition but also underline the potential of quantum computing in real-world applications requiring high precision and robustness. The proposed EQCNN framework is generalizable and can be extended to other domains involving complex image classification tasks, offering a powerful tool for tackling challenges in quantum-enhanced machine learning.