Continual Palmprint Recognition Without Forgetting

As a promising topic of biometrics, palmprint recognition helps to effectively verify a person's identity, which is suitable for building a security system. Recent progress has achieved high recognition accuracy in different benchmark datasets due to deep learning. However, these applications are almost implemented in one dataset with iterative training epochs to help neural network generalize. When applied practically where many new users' palmprints registered in sequence, deep learning-based recognition systems cannot avoid the problem of catastrophic forgetting. In this paper, we propose a continual learning framework based on reinforcement learning to dynamically expand the neural network when facing newly registered palmprints without costly retraining or fine-tuning. Experiments on different datasets demonstrate the high adaptability of our model that is promising for solving the forgetting attack of every biometric system.