Dental forensic identification targets to identify persons with dental traces. The task is vital for the investigation of criminal scenes and mass disasters because of the resistance of dental structures and the wide-existence of dental imaging. However, no widely accepted automated solution is available for this labour-costly task. In this work, we pioneer to study deep learning for dental forensic identification based on panoramic radiographs. We construct a comprehensive benchmark with various dental variations that can adequately reflect the difficulties of the task. By considering the task's unique challenges, we propose FoID, a deep learning method featured by: (i) clinical-inspired attention localization, (ii) domain-specific augmentations that enable instance discriminative learning, and (iii) transformer-based self-attention mechanism that dynamically reasons the relative importance of attentions. We show that FoID can outperform traditional approaches by at least 22.98% in terms of Rank-1 accuracy, and outperform strong CNN baselines by at least 10.50% in terms of mean Average Precision (mAP). Moreover, extensive ablation studies verify the effeteness of each building blocks of FoID. Our work can be a first step towards the automated system for forensic identification among large-scale multi-site databases. Also, the proposed techniques, e.g., self-attention mechanism, can also be meaningful for other identification tasks, e.g., pedestrian re-identification.