PFIG-Palm: Controllable Palmprint Generation via Pixel and Feature Identity Guidance.

Palmprint recognition offers a promising solution for convenient and private authentication. However, the scarcity of large-scale palmprint datasets constrains its development and application. Recent approaches have sought to mitigate this issue by synthesizing palmprints based on Bézier curves. Due to the lack of paired data between curves and palmprints, it is difficult to generate curve-driven palmprints with precise identity. To address this challenge, we propose a novel Pixel and Feature Identity Guidance (PFIG) framework to synthesize realistic palmprints, whose IDs are strictly governed by the Bézier curves. In order to establish ID mapping, an ID Injection (IDI) module is constructed to synthesize pseudo-paired data. Two cross-domain ID consistency losses at pixel and feature levels are further proposed to strictly preserve the semantic information of the input ID curves. Experimental results demonstrate that our ID-guided approach can synthesize more realistic palmprints with controllable identities. Based on only 80,000 synthesized palmprints for pre-training, the recognition accuracy can be improved by more than 18% in terms of TAR@1e-6. When trained exclusively on synthetic data, our method achieves superior performance to existing synthetic approaches. The source code is available at https://github.com/YuchenZou/PFIG-Palm.