Angelo Genovese

Touchless Fingerprint Biometrics: A Survey on 2D and 3D Technologies

Traditional fingerprint recognition systems require that the users touch a sensor to perform biometric acquisitions. In order to increase the usability, acceptability, and accuracy of fingerprint recognition technologies, touchless systems have recently been studied. With respect to touch-based biometric techniques, these systems present important differences in most of the steps of the recognition process.Studies in the literature can be classified into technologies based on two-dimensional and three-dimensional methods. These studies also present important differences in terms of accuracy and required level of user cooperation. This paper presents a brief survey on touchless recognition technologies, proposing a classification of two-dimensional and three-dimensional biometric recognition techniques.

Contactless fingerprint recognition: A neural approach for perspective and rotation effects reduction

Contactless fingerprint recognition systems are being researched in order to reduce intrinsic limitations of traditional biometric acquisition technologies, encompassing the release of latent fingerprints on the sensor platen, non-linear spatial distortions in the captured samples, and relevant image differences with respect to the moisture level and pressure of the fingertip on the sensor surface.Fingerprint images captured by single cameras, however, can be affected by perspective distortions and deformations due to incorrect alignments of the finger with respect to the camera optical axis. These non-idealities can modify the ridge pattern and reduce the visibility of the fingerprint details, thus decreasing the recognition accuracy. Some systems in the literature overcome this problem by computing three-dimensional models of the finger. Unfortunately, such approaches are usually based on complex and expensive acquisition setups, which limit their portability in consumer devices like mobile phones and tablets. In this paper, we present a novel approach able to recover perspective deformations and improper fingertip alignments in single camera systems. The approach estimates the orientation difference between two contactless fingerprint acquisitions by using neural networks, and permits to register the considered samples by applying the estimated rotation angle to a synthetic three-dimensional model of the finger surface. The generalization capability of neural networks offers a significant advantage by allowing processing a robust estimation of the orientation difference with a very limited need of computational resources with respect to traditional techniques. Experimental results show that the approach is feasible and can effectively enhance the recognition accuracy of single-camera biometric systems. On the evaluated dataset of 800 contactless images, the proposed method permitted to decrease the equal error rate of the used biometric system from 3.04% to 2.20%.

Fast 3-D fingertip reconstruction using a single two-view structured light acquisition

Current contactless fingertip recognition systems based on three-dimensional finger models mostly use multiple views (N > 2) or structured light illumination with multiple patterns projected over a period of time. In this paper, we present a novel methodology able to obtain a fast and accurate three-dimensional reconstruction of the fingertip by using a single two-view acquisition and a static projected pattern. The acquisition setup is less constrained than the ones proposed in the literature and requires only that the finger is placed according to the depth of focus of the cameras, and in the overlapping field of views. The obtained pairs of images are processed in order to extract the information related to the fingertip and the projected pattern. The projected pattern permits to extract a set of reference points in the two images, which are then matched by using a correlation approach. The information related to a previous calibration of the cameras is then used in order to estimate the finger model, and one input image is wrapped on the resulting three-dimensional model, obtaining a three-dimensional pattern with a limited distortion of the ridges. In order to obtain data that can be treated by traditional algorithms, the obtained three-dimensional models are then unwrapped into bidimensional images. The quality of the unwrapped images is evaluated by using a software designed for contact-based fingerprint images. The obtained results show that the methodology is feasible and a realistic three-dimensional reconstruction can be achieved with few constraints. These results also show that the fingertip models computed by using our approach can be processed by both specific three-dimensional matching algorithms and traditional matching approaches. We also compared the results with the ones obtained without using structured light techniques, showing that the use of a projector achieves a faster and more accurate fingertip reconstruction.

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

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.

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

Biometric systems identify individuals by comparison of the individual biometric traits, such as the fingerprint patterns. In the literature, many relevant methods are based on the localization of a reference “pivot” point of the fingerprint, called principal singular point (PSP). Most of the time, the PSP is selected from the list of the estimated singular points (SPs) that are identified by specific local patterns of the fingerprint ridges, called cores and deltas. The challenge is to provide an automatic method capable to select the same PSP from different images of the same fingertip. In this paper, we propose a technique that estimates the position of all the singular points by processing the global structure of the ridges and extracting a specific set of features. The selection of the reference point from the candidate list is then obtained by processing the extracted features with computational intelligence classification techniques. Experiments show that the method is accurate and it can be applied on contact and contact-less image types.

Quality measurement of unwrapped three-dimensional fingerprints: A neural networks approach

Traditional biometric systems based on the fingerprint characteristics acquire the biometric samples using touch-based sensors. Some recent researches are focused on the design of touch-less fingerprint recognition systems based on CCD cameras. Most of these systems compute three-dimensional fingertip models and then apply unwrapping techniques in order to obtain images compatible with biometric methods designed for images captured by touch-based sensors. Unwrapped images can present different problems with respect to the traditional fingerprint images. The most important of them is the presence of deformations of the ridge pattern caused by spikes or badly reconstructed regions in the corresponding three-dimensional models. In this paper, we present a neural-based approach for the quality estimation of images obtained from the unwrapping of three-dimensional fingertip models. The paper also presents different sets of features that can be used to evaluate the quality of fingerprint images. Experimental results show that the proposed quality estimation method has an adequate accuracy for the quality classification. The performances of the proposed method are also evaluated in a complete biometric system and compared with the ones obtained by a well-known algorithm in the literature, obtaining satisfactory results.

Iris Segmentation: State of the Art and Innovative Methods

Iris recognition is nowadays considered as one of the most accurate biometric recognition techniques. However, the overall performances of such systems can be reduced in non-ideal conditions, such as unconstrained, on-the-move, or non-collaborative setups.

Virtual environment for synthetic smoke clouds generation

In this paper we propose a virtual environment for the creation of synthetic wildfire smoke frame sequences, able to simulate a distant smoke plume and to integrate it with an existing frame sequence. This work provides a virtual tool to measure the accuracy of existing image-based wildfire smoke detection systems without the need to produce real smoke and fires in the environments. The proposed algorithm uses a cellular model driven by the rules of propagation and collision to simulate the basic physical principles of advection, diffusion, buoyancy, and the response to external forces (such as the wind). Adverse environmental conditions like fog and low-light are also simulated, together with the introduction of noise in order to reproduce acquisition defects. The resulting frame sequences are then evaluated by using a smoke detection system, which shows that our method for virtual smoke simulation gives results comparable to real situations. The extracted data can then be used to increase the performance of smoke detection systems when few real data are available.

Accurate 3D fingerprint virtual environment for biometric technology evaluations and experiment design

Three-dimensional models of fingerprints obtained from contactless acquisitions have the advantages of reducing the distortion present in traditional contact-based samples and the effects of dirt on the finger and the sensor surface. Moreover, they permit to use a greater area for the biometric recognition. The design and test of three-dimensional reconstruction algorithms and contactless recognition methods require the collection of large databases. Since this task can be expensive and timeconsuming, some methods in the literature deal with the generation of synthetic biometric samples. At the best of our knowledge, however, there is only a preliminary study on the computation of small areas of synthetic three-dimensional fingerprints. In this paper, we extend our previous work and describe a virtual environment for the generation of complete threedimensional fingertip shapes, which can be useful for the research community working in the field of three-dimensional fingerprint biometrics. The method is based on image processing techniques and algorithms designed for biometric recognition. We validated the realism of the simulated models by comparing them with real contactless acquisitions. Results show that the method is feasible and produces realistic three-dimensional samples which can effectively be processed by biometric recognition algorithms.

Automatic Classification of Acquisition Problems Affecting Fingerprint Images in Automated Border Controls

Automated Border Control (ABC) systems are technologies designed to increase the speed and accuracy of the identity verifications performed at international borders. A great number of ABCs deployed in different countries use fingerprint recognition techniques because of their high accuracy and user acceptability. However, the accuracy of fingerprint recognition methods can drastically decrease in this application context due to user-sensor interaction factors. This paper presents two main contributions. The first of them consists in an experimental evaluation performed to search the main negative aspects that could affect the usability and accuracy in ABCs based on fingerprint biometrics. The mainly considered aspects consists in the presence of luggage and cleanness of the finger skin. The second contribution consists in a novel approach for automatically identifying the type of user-sensor interaction that caused quality degradations in fingerprint samples. This method uses a specific feature set and computational intelligence techniques to detect non-idealities in the acquisition process and to suggest corrective actions to travelers and border guards. To the best of our knowledge, this is the first method in the literature designed to detect problems in user-sensor interaction different from improper pressures on the acquisition surface. We validated the proposed approach using a dataset of 2880 images simulating different scenarios typical of ABCs. Results shown that the proposed approach is feasible and can obtain satisfactory performance, with a classification error of 0.098.