Improving Latent Fingerprint Orientation Field Estimation Using Inpainting Techniques

Zheng Wang, Zexi Jia, Chuanwei Huang, Hongyan Fei, Song Wu, Jufu Feng

Abstract: Latent fingerprints play a vital role in forensic investigations. However, accurately estimating their orientation field can be challenging due to complex noise or overlapping fingerprint regions. In this paper, we propose a method to identify and correct these regions in the orientation field estimation. Specifically, our method comprises two networks: the first is an orientation field estimation network that outputs the initial orientation field, segment, and quality map, which determines the low-quality regions, including overlapping fingerprints and unclear ridge areas. The second network refills the orientation field in low-quality regions using inpainting techniques. This effectively handles unclear ridges and overlapping fingerprints, which can disrupt orientation field estimation. We assess our method using the NIST SD 27 dataset and demonstrate superior performance compared to existing state-of-the-art latent orientation field estimation methods, achieving the average root mean square deviation of 11.20.