Identity Verification

Identity verification research aims to develop reliable and secure methods for confirming an individual's identity, focusing on accuracy, fairness, and privacy. Current efforts concentrate on improving biometric authentication using various modalities (e.g., facial, voice, gait, keystroke dynamics) and advanced machine learning models like convolutional neural networks, recurrent neural networks, and transformers, often incorporating techniques like federated learning and synthetic data generation to address privacy concerns. These advancements have significant implications for enhancing security in online transactions, access control, and various other applications while simultaneously mitigating biases and ensuring equitable performance across diverse demographics.