Multi-constrained Orientation Field Modeling and Its Application for Fingerprint Indexing
Fingerprint orientation field, representing the fingerprint ridge-valley structure direction, plays an essential role in fingerprint preprocessing tasks. Orientation field is able to be reconstructed by either non-parameterized or parameterized methods. In this paper, we propose a new parameterized approach for orientation field modeling. The proposed algorithm minimizes a composite model including three constraints corresponding to a least square data fitting term, a total variation regularization and a \(L_1\) sparse regularization. This model has been shown to be very effective for fingerprint orientation field reconstruction. Furthermore, its effectiveness has been proven by several experiments. First, the experiments on poor-quality fingerprint images are conducted. Visual comparisons demonstrate the robustness of the proposed method when processing noisy fingerprint images. Then, as another application of the proposed model, its resultant sparse representation is employed for fingerprint indexing. The experiments on FVC 2000 DB2a and FVC 2002 DB1a datasets show the superior performance of the proposed model for fingerprint indexing.
KeywordsSparse Representation Penetration Rate Fingerprint Image Short Time Fourier Transform Structural Noise
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