Measurement of the principal singular point in contact and contactless fingerprint images by using computational intelligence techniques

IEEE Trans. Syst. Man Cybern, Syst.

Genovese

Piuri

et al. 2016

Self Cite

Touchless fingerprint recognition systems do not require contact of the finger with any acquisition surface and thus provide an increased level of hygiene, usability, and user acceptability of fingerprint-based biometric technologies. The most accurate touchless approaches compute 3-D models of the fingertip. However, a relevant drawback of these systems is that they usually require constrained and highly cooperative acquisition methods. We present a novel, fully touchless fingerprint recognition system based on the computation of 3-D models. It adopts an innovative and less-constrained acquisition setup compared with other previously reported 3-D systems, does not require contact with any surface or a finger placement guide, and simultaneously captures multiple images while the finger is moving. To compensate for possible differences in finger placement, we propose novel algorithms for computing 3-D models of the shape of a finger. Moreover, we present a new matching strategy based on the computation of multiple touch-compatible images. We evaluated different aspects of the biometric system: acceptability, usability, recognition performance, robustness to environmental conditions and finger misplacements, and compatibility and interoperability with touch-based technologies. The proposed system proved to be more acceptable and usable than touch-based techniques. Moreover, the system displayed satisfactory accuracy, achieving an equal error rate of 0.06% on a dataset of 2368 samples acquired in a single session and 0.22% on a dataset of 2368 samples acquired over the course of one year. The system was also robust to environmental conditions and to a wide range of finger rotations. The compatibility and interoperability with touch-based technologies was greater or comparable to those reported in public tests using commercial touchless devices.

Section: Previous Work a Two-dimensional Systemsmentioning

confidence: 99%

Toward Unconstrained Fingerprint Recognition: A Fully Touchless 3-D System Based on Two Views on the Move

IEEE Trans. Syst. Man Cybern, Syst.

Genovese

Piuri

et al. 2016

Self Cite

“…For instance: They do not require the contact of the traveler, i.e., are contactless; permit the acquisition at higher distances or while the traveler is moving; or allow the system to use natural light conditions. Some of the most promising techniques with regard to e-Gates are contactless fingerprint recognition [58]- [66], contactless palm recognition [67] and iris recognition at a distance [57], [68].…”

Section: E Unconstrained Biometricsmentioning

confidence: 99%

Advanced design of Automated Border Control gates: Biometric system techniques and research trends

2015 IEEE International Symposium on Systems Engineering (ISSE)

Genovese

Muñoz

et al. 2015

Self Cite

Last few years have witnessed an ever-increasing demand of border crossing, whose processing introduces the need to speed-up the clearance process at the Border Crossing Points (BCP). Automated Border Control (ABC) gates, or shortly e-Gates, can verify the identity of the travelers crossing the borders by exploiting their biometric traits, without the need of a constant human intervention. Biometric technologies have a relevant impact on the improvement of efficiency, effectiveness and security of the checking processes. Automated biometric recognition can increase the border processing throughput of the BCP, as well as facilitate the clearance procedures. To grant the passage of the border, the e-Gate compares the biometric samples of the traveler stored into the electronic document with live acquisitions. This paper presents the latest substantial advances in the design of e-Gates. In particular, it presents the Biometric Verification System in detail, including its hardware and software components, as well as the procedures followed during the biometric verification of the traveler's identity. We address the complex issue of measuring the performance of an ABC system, considering the real applicability of the figures of merit usually adopted in biometric system's evaluation. To complete the view of the current e-Gates, we highlight the main challenges and the research trends relating to the biometric systems currently used in e-Gates.

“…The proposed method is similar to the method described in [16]. This method can be divided in four distinct steps:…”

Section: A Enhancement Of the Images Roi Selectionmentioning

confidence: 99%

“…The ROI estimation is performed by using an algorithm based on the local standard deviation of the image, which is described in [16], and by removing the shadow of the finger by a threshold operation.…”

Section: A Enhancement Of the Images Roi Selectionmentioning

confidence: 99%

See 1 more Smart Citation

A neural-based minutiae pair identification method for touch-less fingerprint images

2011 IEEE Workshop on Computational Intelligence in Biometrics and Identity Management (CIBIM)

Piuri

Scotti

2011

Self Cite

Abstract-Contact-based sensors are the traditional devices used to capture fingerprint images in commercial and homeland security applications. Contact-less systems achieve the fingerprint capture by vision systems avoiding that users touch any parts of the biometric device. Typically, the finger is placed in the working area of an optics system coupled with a CCD module. The captured light pattern on the finger is related to the real ridges and valleys of the user fingertip, but the obtained images present important differences from the traditional fingerprint images. These differences are related to multiple factors such as light, focus, blur, and the color of the skin. Unfortunately, the identity comparison methods designed for fingerprint images captured with touch-based sensors do not obtain sufficient accuracy when are directly applied to touch-less images. Recent works show that multiple views analysis and 3D reconstruction can enhance the final biometric accuracy of such systems. In this paper we propose a new method for the identification of the minutiae pairs between two views of the same finger, an important step in the 3D reconstruction of the fingerprint template. The method is divisible in the sequent tasks: first, an image preprocessing step is performed; second, a set of candidate minutiae pairs is selected in the two images, then a list of candidate pairs is created; last, a set of local features centered around the two minutiae is produced and processed by a classifier based on a trained neural network. The output of the system is the list of the minutiae pairs present in the input images. Experiments show that the method is feasible and accurate in different light conditions and setup configurations.