Fingerprint Pore Matching Based on Sparse Representation

Abstract

Abstract-This paper proposes an improved direct fingerprint pore matching method. It measures the differences between pores by using the sparse representation technique. The coarse pore correspondences are then established and weighted based on the obtained differences. The false correspondences among them are finally removed by using the weighted RANSAC algorithm. Experimental results have shown that the proposed method can greatly improve the accuracy of existing methods.

PoreNet: CNN-based Pore Descriptor for High-resolution Fingerprint Recognition

“…Overall, the experimental results presented in this section indicate that the proposed approach provides a comparable EER with the current state-of-the-art approach [21] on all the datasets. Most importantly, the proposed PoreNet model 15.42% 7.05% Liu et al [12] 6.59% 0.97% Liu et al [13] 3.25% 0.53% Segundo and Lemes [15] 3.74% 0.76% Dahia and Segundo [19] 3.05% 0.44% Xu et al [21] 1.73% 0.51% Proposed approach 2.56% 0.57% provides lower FMR10000 and FMR1000 values as compared to the current state-of-the-art approach on both the datasets.…”

“…Authors have also shown that pores are effective for recognition using partial fingerprint images, which may not contain sufficient level-2 features [10], [11]. Liu et al [12] proposed an improved direct pore matching approach. Firstly, pores are represented by a local descriptor as in [9].…”

“…Finally, weighted RANSAC (WRANSAC) [18] algorithm is employed to refine the coarse correspondence. Authors in [13] extended the work in [12] by using tangent distance and sparse representation-based matching to compare the pores of reference and probe fingerprint images. The final refinement of the initial correspondences is obtained by employing WRANSAC.…”

Optimum Geometric Transformation and Bipartite Graph-Based Approach to Sweat Pore Matching for Biometric Identification

“…The Jacobian matrix and the parameter vector of the transformation are shown in Table 1. As described in (14), the LMA is a combination of the gradient descent and the Gauss-Newton methods. As a result, the optimal transformation M is estimated, and the global similarity matrix G is then calculated as…”

“…After calculating the correlation between the feature vectors from two different fingerprints, correspondences are determined and then refined by the random sample consensus (RANSAC) algorithm. Liu et al improved the direct pore matching method by introducing the sparse representation [14]. By reducing the minutia-ridge dependency, these methods showed that the recognition accuracy could be improved.…”