Error-Corrected Quantum CNN for Gender-Based Micro-Expression Analysis in Affective Computing

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.