Finger Recovery Transformer: Towards Better Incomplete Fingerprint Identification

Fingerprint recognition is a crucial biometric technology extensively used in identity verification, including areas like criminal investigations, security systems, and biometric authentication. This technology encounters greater challenges when dealing with incomplete fingerprint images, especially those with significant background noise or substantial portions of the fingerprint missing. Existing incomplete fingerprint recognition technologies struggle with extensive data loss, primarily due to the significant reduction and difficulty in extracting usable features from incomplete fingerprint images. Current image processing methods or deep learning models are unable to comprehensively reconstruct fingerprint features with limited information. To address these challenges, we introduce the Finger Recovery Transformer (FingerRT), an innovative network specifically designed for recovering incomplete fingerprint information. FingerRT can simultaneously complete ambient noise cancellation and fingerprint feature information recovery, resulting in a complete and clean fingerprint image. FingerRT combines the most critical feature information in fingerprints, directional field, and minutiae, as supervision information. FingerRT inherits the denoising ability of the fingerprint enhancement networks and the powerful generative ability of the Vision Transformer architecture, enabling high-quality and robust fingerprint information recovery. By imposing constraints at multiple levels, including fingerprint features, fingerprint images, and multi-stage generation, FingerRT can complete fingerprint information accurately and effectively. Experiments demonstrate that FingerRT significantly enhances fingerprint recognition accuracy after recovery across various fingerprint datasets, including rolled, snapped, and latent fingerprints.