FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data

• URL: http://arxiv.org/abs/2310.19024v1
• Date: Sun, 29 Oct 2023 14:30:01 GMT
• Title: FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
• Authors: Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch, Ori Nizan, Igor Kviatkovsky, Joshua Engelsma, Manoj Aggarwal, Gerard Medioni
• Abstract summary: We present FPGAN-Control, an identity preserving image generation framework. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity level, degree of appearance control, and low synthetic-to-real domain gap.
• Score: 7.203557048672379
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Training fingerprint recognition models using synthetic data has recently gained increased attention in the biometric community as it alleviates the dependency on sensitive personal data. Existing approaches for fingerprint generation are limited in their ability to generate diverse impressions of the same finger, a key property for providing effective data for training recognition models. To address this gap, we present FPGAN-Control, an identity preserving image generation framework which enables control over the fingerprint's image appearance (e.g., fingerprint type, acquisition device, pressure level) of generated fingerprints. We introduce a novel appearance loss that encourages disentanglement between the fingerprint's identity and appearance properties. In our experiments, we used the publicly available NIST SD302 (N2N) dataset for training the FPGAN-Control model. We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap. Finally, training recognition models using only synthetic datasets generated by FPGAN-Control lead to recognition accuracies that are on par or even surpass models trained using real data. To the best of our knowledge, this is the first work to demonstrate this.

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