Abstract
The de-noising of the fingerprint image is one of the key tasks before the extraction of the minutiae in automatic fingerprint matching. When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local minutiae in the fingerprint image very well. To address this problem, we propose a local orientation field based nonlocal means (NLM-LOF) method in this paper. Experimental results on the simulated and real images show that the proposed method can suppress noise effectively while preserving edges and details in the fingerprint image and it outperforms the state-of-art nonlocal means method in terms of qualitative metrics and visual comparisons.
Highlights
As one of the most important biometric technologies, fingerprint identification has been widely used in identity recognition
When used for de-noising the fingerprint image, the nonlocal means method can not preserve the local minutiae in the fingerprint image very well
Due to complex identify conditio- ns, the acquired fingerprint images are usually contaminated with noise which is disadvantageous for the minutiae extraction
Summary
As one of the most important biometric technologies, fingerprint identification has been widely used in identity recognition. In [4], Bayesian de-noising in the wavelet domain was presented to realize fingerprint image de-noising. All these methods tend to damage edges and details in the fingerprint images because they only use local information in the images. Reprojections (NLM-R) method [6], the NLM using Shape Adaptive Patches (NLM-SAP) [7] These improved methods perform better than the traditional NLM, they cannot preserve fringes and minutiaes effectively. Compared with above state-of-art de-noising methods, the proposed method is more robust and it can preserve minutiae better while suppressing noise in the fingerprint images
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