International Journal of Science, Engineering and TechnologyAuthors: V. Sujatha, B. Sri Sai Kavya Samhitha, B. Vyshnavi, M. Mallika, P. Teja
Abstract: Facial expression-based emotion recognition plays a crucial role in sectors such as healthcare, security, education, and human-computer interaction, yet achieving accuracy, privacy, and security remains challenging. This research proposes a secure and reliable emotion recognition framework by integrating Deep Convolutional Neural Networks (DCNN), blockchain technology, and Quantum Cryptographic Distribution (QCD). The framework employs a DCNN trained on diverse datasets to ensure resilience against variations in lighting, occlusion, and facial diversity. It captures facial images or video frames, extracts spatial and texture-based features, and utilizes these features for emotion classification. QCD provides post-quantum cryptographic security for data transmission, while anonymized results, timestamps, and hashed feature vectors are securely stored on a permissioned blockchain. Experimental evaluation on benchmark datasets (FER2013, CK+, and JAFFE) demonstrates superior performance compared to traditional CNN, VGG16, and ResNet50 models, achieving 97.8% accuracy, high precision (~0.98), recall (~0.98), and F1-score (0.98). The system is suitable for privacy-sensitive applications as it ensures tamper-proof, auditable, and real-time emotion recognition. By combining blockchain immutability, quantum-secured communication, and AI-driven classification, the proposed framework offers a secure, interpretable, and future-proof solution for emotion recognition in practical applications.
DOI: http://doi.org/10.5281/zenodo.20390297
