<p>Emotion recognition from facial expressions is a fundamental challenge in affective computing due to variability, context, cultural differences, and subtlety of expressions. Traditional approaches have predominantly relied on classical machine learning techniques and neural networks to decipher emotional cues from facial images. In this work, a framework for emotion recognition using facial images that leverage the power of Quantum Convolutional Neural Network (QCNN) is presented. Drawing inspiration from the principles of quantum computing, the proposed Quantum-inspired Convolutional Neural Network (QiCNN) approach revolutionizes how emotional features are captured, processed, and analyzed. The proposed model encodes intricate emotional nuances within quantum states by harnessing quantum-inspired operations, enabling enhanced feature extraction and fusion. Moreover, the performance of the proposed model was evaluated on three emotion datasets, i.e., the CK+, FER2013, and AffectNet, showcasing its potential advantages for reliability and robustness. Also, a comparative evaluation of the proposed model with the conventional Convolutional Neural Network (CNN) technique in terms of accuracy and execution time is done along with the analysis of the proposed model performance with other recent contemporary methods.</p>