Hybrid-Quantum CNN for Enhanced Facial Emotion Recognition: A Comparative Study with VGG16 on the RAF-DB Dataset

Abstract

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.