Learning Minutiae Neighborhoods: A New Binary Representation for Matching Fingerprints

Abstract

Representation of fingerprints is one of the key factors that limits the accuracy and efficiency of matching algorithms. Most popular methods represent each fingerprint as an unordered set of minutiae with variable cardinality and the matching algorithms are left with the task of finding the best correspondence between the two sets of minutiae. While this makes the representation more flexible and matching more accurate, the task becomes computationally intensive. Fixed length representations with aligned features are highly efficient to match. However, creating an aligned representation without the knowledge of the sample to which it is to be matched, makes the problem of representation more complex. Some of the fixed-length representations only provide partial alignment, leaving the rest to the matching stage. In this paper, we propose a fixed length representation for fingerprints that provides exact alignment between the features, thus enabling high-speed matching with minimal computational effort. The representation extends the idea of object representation using bag of words into a bag of minutiae neighborhoods. The representation is provably invariant to affine transformations (rotation, translation and uniform scaling), and is shown to be highly discriminative for the task of verification. Experimental results on FVC 2002 and 2004 datasets clearly show the superiority of the representation with competing methods. As the proposed representation can be computed from the standard minutiae templates, the method is applicable to existing datasets, where the original fingerprint images are not available.

Reconstruction of Fingerprint Shape using Fractal Interpolation

“…If a pixel is in a region where the low luminance (lower luminance to a set ready level value), then it is replaced by the value 0 (black color). However, if a pixel is in a region where the luminance is clearly defined, it is replaced by the value 1 (white color) [10][11].…”

“…Some pretreatment and enhancement steps are frequently performed to simplify the minutiae extraction task [4][5][6][7][8]. The first step of the algorithm concerns the fingerprint image segmentation; this phase requires the conversion of the grayscale fingerprints image into a binary image [9][10][11]. The binary images obtained by the binarization process are generally subjected to a thinning step [12][13] which makes it possible to reduce the thickness of the peak line to one pixel [14][15].…”

Producing secure multimodal biometric descriptors using artificial neural networks

“…Candidates cannot be enrolled by directly matching the pixel values of the images collected for their biometrics, such as the face, iris and fingerprint, as these images are different from each other even for the same subject. Such a difference can occur for a wide range of reasons, such as the positioning of the part that the template is being collected from with respect to the sensor that is collecting the template or to the dynamic nature of these biometrics, for example illumination, skin wounds and scratches when collecting fingerprint or the facial hair and accessories when collecting facial images [12–14]. Instead, a descriptor is created based on the distinctive features that exist in the template, so that, matching can be conducted based on the difference between these descriptors [15,16].…”

Entropy Measurement for Biometric Verification Systems

“…If the system decision threshold, which affects the system false acceptance rate (FAR) and rejection rate, is also known, an attack strategy can be designed to defeat the system effectively. In this paper, we will focus on binary biometric representation, as binary representation (e.g., binary finger [35], iris [28], palm [30], and face [5], [10], [19] representations) has been a common form of biometric representation for the application of error-correcting-code-based template protection schemes.…”