Minutia tensor matrix: a new strategy for fingerprint matching.

Establishing correspondences between two minutia sets is a fundamental issue in fingerprint recognition. This paper proposes a new tensor matching strategy. First, the concept of minutia tensor matrix (simplified as MTM) is proposed. It describes the first-order features and second-order features of a matching pair. In the MTM, the diagonal elements indicate similarities of minutia pairs and non-diagonal elements indicate pairwise compatibilities between minutia pairs. Correct minutia pairs are likely to establish both large similarities and large compatibilities, so they form a dense sub-block. Minutia matching is then formulated as recovering the dense sub-block in the MTM. This is a new tensor matching strategy for fingerprint recognition. Second, as fingerprint images show both local rigidity and global nonlinearity, we design two different kinds of MTMs: local MTM and global MTM. Meanwhile, a two-level matching algorithm is proposed. For local matching level, the local MTM is constructed and a novel local similarity calculation strategy is proposed. It makes full use of local rigidity in fingerprints. For global matching level, the global MTM is constructed to calculate similarities of entire minutia sets. It makes full use of global compatibility in fingerprints. Proposed method has stronger description ability and better robustness to noise and nonlinearity. Experiments conducted on Fingerprint Verification Competition databases (FVC2002 and FVC2004) demonstrate the effectiveness and the efficiency

High-Resolution Mobile Fingerprint Matching via Deep Joint KNN-Triplet Embedding

In mobile devices, the limited area of fingerprint sensors brings demand of partial fingerprint matching. Existing fingerprint authentication algorithms are mainly based on minutiae matching. However, their accuracy degrades significantly for partial-to-partial matching due to the lack of minutiae. Optical fingerprint sensor can capture very high-resolution fingerprints (2000dpi) with rich details as pores, scars, etc. These details can cover the shortage of minutiae insufficiency. In this paper, we propose a novel matching algorithm for such fingerprints, namely Deep Joint KNN-Triplet Embedding, by making good use of these subtle features. Our model employs a deep convolutional neural network (CNN) with a well-designed joint loss to project raw fingerprint images into an Euclidean space. Then we can use L2-distance to measure the similarity of two fingerprints. Experiments indicate that our model outperforms several state-of-the-art approaches

Illustration of the main idea in our method.

<p>The fingerprint images are from FVC2004 DB1 27–3 and 27–5. For clarity, only a small subset of minutia pairs are shown. Candidate minutia pairs shown in (a) form the correspondence graph in (b) and the minutia tense matrix (<i>MTM</i>) in (c). Genuine minutia pairs corresponds to the dense subgraph of correspondence graph, and also the dense block of <i>MTM</i>. Minutia matching is formulated as recovering the dense sub-block in the <i>MTM</i>. It can be solved by the spectral correspondence methods.</p

On learning with dissimilarity functions

We study the problem of learning a classification task in which only a dissimilarity function of the objects is accessible. That is, data are not represented by feature vectors but in terms of their pairwise dissimilarities. We investigate the sufficient conditions for dissimilarity functions to allow building accurate classifiers. Our results have the advantages that they apply to unbounded dissimilarities and are invariant to order-preserving transformations. The theory immediately suggests a learning paradigm: construct an ensemble of decision stumps each depends on a pair of examples, then find a convex combination of them to achieve a large margin. We next develop a practical algorithm called Dissimilarity based Boosting (DBoost) for learning with dissimilarity functions under the theoretical guidance. Experimental results demonstrate that DBoost compares favorably with several existing approaches on a variety of databases and under different conditions. 1

A complete discriminative subspace for robust face recognition

Aimed at the problem that linear discriminative analysis algorithms for face recognition usually miss discriminative information when reducing dimensions, and the problem that it is difficult to make full use of discriminative information both in rank space and null space at the same time, this paper proposes a novel method to construct a new subspace called complete discriminative subspace and its discriminant matrix without losing any discriminative information contained in original space. The dimension of the new subspace is much lower and the constructing procedure is simple and costless. The experimental results demonstrate that this method has better performance and efficiency than standard discriminative analysis algorithms for face recognition.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000351597603155&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Imaging Science &amp; Photographic TechnologyCPCI-S(ISTP)

Curves of <i>EER</i> and <i>Time</i> with the growth of structural radius on <i>FVC2004-DB1</i>.

<p>Curves of <i>EER</i> and <i>Time</i> with the growth of structural radius on <i>FVC2004-DB1</i>.</p

Time on <i>FVC2004-DB1</i> for global matching.

<p><i>EMF</i>, <i>LSS</i>, <i>LTS</i>, <i>MT</i>, <i>MCC</i>, <i>LSC</i>, <i>GF</i>, <i>MCC</i> + <i>MTM</i>, <i>MTM</i> + <i>MTM</i> are nine algorithms for global matching. This table shows the average matching time (<i>ms</i>) for per match on <i>FVC2004-DB1</i>.</p><p>Time on <i>FVC2004-DB1</i> for global matching.</p