Facial Biometric Authentication Enhanced by Emotional and Demographic Contexts

This paper presents a cloud-based facial biometric authentication system integrating demographic and emotional attributes to enhance verification robustness under compression and distortion. A prototype web platform was implemented using cloud-based services, combining facial recognition with demographic and emotional analysis. The system was experimentally evaluated under multiple image degradation scenarios, including lossy and lossless compression, blurring, and block partitioning. Results show that recognition accuracy remains above 98% under mild compression, but decreases significantly under severe distortions, with match rates dropping below 25% in extreme cases. Additional experiments demonstrate reliable gender classification (95%) and moderate performance for age and emotion estimation. The contribution of this work lies in demonstrating the practical feasibility of combining identity verification with contextual biometric features in resource-constrained environments, while also outlining the limitations and security implications of such systems.