Bias-Resilient Framework for Emotion Prediction Using Facial Recognition in Real-World Applications

Abstract

Emotion prediction through facial recognition has gained significant attention in recent years due to its transformative applications in healthcare, education, security, and human-computer interaction. By analyzing facial expressions to infer emotional states, this technology enables seamless and intuitive interactions. However, its deployment in real-world applications is often hindered by privacy concerns, demographic biases, and a lack of robustness in diverse environments. This project introduces a Privacy-Preserving and Bias-Resilient Framework for Emotion Prediction Using Facial Recognition, aimed at addressing these challenges. The proposed solution incorporates privacy-preserving techniques such as encryption and anonymization to safeguard sensitive facial data while ensuring compliance with global privacy regulations like GDPR. Bias in prediction models is mitigated through the use of diverse datasets and fairness algorithms, ensuring equitable performance across demographic groups. Furthermore, the framework is designed for robustness in real-world conditions, tackling issues such as dynamic lighting, varied facial expressions, and adversarial attacks. The framework is implemented using state-of-the-art deep learning techniques and validated through rigorous testing in controlled and real-world scenarios