Integrating explainable AI with synthetic biometric data for enhanced image synthesis and privacy in computer vision systems

Integrating Explainable AI (XAI) with synthetic biometric data improves image synthesis and privacy in computer vision systems by generating high-quality images while ensuring interpretability. This integration enhances trust and transparency in AI-driven biometric applications. However, traditional biometric data collection methods face challenges such as privacy risks, data scarcity, biases, and regulatory constraints, limiting their effectiveness in authentication and identity verification. To address these limitations, we propose a Generative Adversarial Networks with Explainable AI (GAN-EAI) framework for privacy-preserving biometric image synthesis. This framework utilizes GANs to generate high-fidelity synthetic biometric images while incorporating XAI techniques to interpret and validate the generated outputs, ensuring fairness, robustness, and bias mitigation. The proposed method enables secure, privacy-conscious biometric image synthesis, making it suitable for applications in authentication, healthcare, and identity verification. By leveraging explainability, it ensures that the model's decision-making process is interpretable, reducing the risk of biased or adversarial outputs. Experimental results demonstrate that GAN-EAI achieves superior image quality, enhances privacy protection, and reduces bias in synthetic biometric datasets, making it a reliable solution for real-world biometric applications. This research highlights the potential of integrating explainability with generative models to advance privacy-preserving AI in computer vision.