CONTACTLESS FINGERPRINT BIOMETRICS: ACQUISITION, PROCESSING, AND PRIVACY PROTECTION

Biometrics is defined by the International Organization for Standardization (ISO) as \u201cthe automated recognition of individuals based on their behavioral and biological characteristics\u201d Examples of distinctive features evaluated by biometrics, called biometric traits, are behavioral characteristics like the signature, gait, voice, and keystroke, and biological characteristics like the fingerprint, face, iris, retina, hand geometry, palmprint, ear, and DNA. The biometric recognition is the process that permits to establish the identity of a person, and can be performed in two modalities: verification, and identification. The verification modality evaluates if the identity declared by an individual corresponds to the acquired biometric data. Differently, in the identification modality, the recognition application has to determine a person's identity by comparing the acquired biometric data with the information related to a set of individuals. Compared with traditional techniques used to establish the identity of a person, biometrics offers a greater confidence level that the authenticated individual is not impersonated by someone else. Traditional techniques, in fact, are based on surrogate representations of the identity, like tokens, smart cards, and passwords, which can easily be stolen or copied with respect to biometric traits. This characteristic permitted a wide diffusion of biometrics in different scenarios, like physical access control, government applications, forensic applications, logical access control to data, networks, and services. Most of the biometric applications, also called biometric systems, require performing the acquisition process in a highly controlled and cooperative manner. In order to obtain good quality biometric samples, the acquisition procedures of these systems need that the users perform deliberate actions, assume determinate poses, and stay still for a time period. Limitations regarding the applicative scenarios can also be present, for example the necessity of specific light and environmental conditions. Examples of biometric technologies that traditionally require constrained acquisitions are based on the face, iris, fingerprint, and hand characteristics. Traditional face recognition systems need that the users take a neutral pose, and stay still for a time period. Moreover, the acquisitions are based on a frontal camera and performed in controlled light conditions. Iris acquisitions are usually performed at a distance of less than 30 cm from the camera, and require that the user assume a defined pose and stay still watching the camera. Moreover they use near infrared illumination techniques, which can be perceived as dangerous for the health. Fingerprint recognition systems and systems based on the hand characteristics require that the users touch the sensor surface applying a proper and uniform pressure. The contact with the sensor is often perceived as unhygienic and/or associated to a police procedure. This kind of constrained acquisition techniques can drastically reduce the usability and social acceptance of biometric technologies, therefore decreasing the number of possible applicative contexts in which biometric systems could be used. In traditional fingerprint recognition systems, the usability and user acceptance are not the only negative aspects of the used acquisition procedures since the contact of the finger with the sensor platen introduces a security lack due to the release of a latent fingerprint on the touched surface, the presence of dirt on the surface of the finger can reduce the accuracy of the recognition process, and different pressures applied to the sensor platen can introduce non-linear distortions and low-contrast regions in the captured samples. Other crucial aspects that influence the social acceptance of biometric systems are associated to the privacy and the risks related to misuses of biometric information acquired, stored and transmitted by the systems. One of the most important perceived risks is related to the fact that the persons consider the acquisition of biometric traits as an exact permanent filing of their activities and behaviors, and the idea that the biometric systems can guarantee recognition accuracy equal to 100\% is very common. Other perceived risks consist in the use of the collected biometric data for malicious purposes, for tracing all the activities of the individuals, or for operating proscription lists. In order to increase the usability and the social acceptance of biometric systems, researchers are studying less-constrained biometric recognition techniques based on different biometric traits, for example, face recognition systems in surveillance applications, iris recognition techniques based on images captured at a great distance and on the move, and contactless technologies based on the fingerprint and hand characteristics. Other recent studies aim to reduce the real and perceived privacy risks, and consequently increase the social acceptance of biometric technologies. In this context, many studies regard methods that perform the identity comparison in the encrypted domain in order to prevent possible thefts and misuses of biometric data. The objective of this thesis is to research approaches able to increase the usability and social acceptance of biometric systems by performing less-constrained and highly accurate biometric recognitions in a privacy compliant manner. In particular, approaches designed for high security contexts are studied in order improve the existing technologies adopted in border controls, investigative, and governmental applications. Approaches based on low cost hardware configurations are also researched with the aim of increasing the number of possible applicative scenarios of biometric systems. The privacy compliancy is considered as a crucial aspect in all the studied applications. Fingerprint is specifically considered in this thesis, since this biometric trait is characterized by high distinctivity and durability, is the most diffused trait in the literature, and is adopted in a wide range of applicative contexts. The studied contactless biometric systems are based on one or more CCD cameras, can use two-dimensional or three-dimensional samples, and include privacy protection methods. The main goal of these systems is to perform accurate and privacy compliant recognitions in less-constrained applicative contexts with respect to traditional fingerprint biometric systems. Other important goals are the use of a wider fingerprint area with respect to traditional techniques, compatibility with the existing databases, usability, social acceptance, and scalability. The main contribution of this thesis consists in the realization of novel biometric systems based on contactless fingerprint acquisitions. In particular, different techniques for every step of the recognition process based on two-dimensional and three-dimensional samples have been researched. Novel techniques for the privacy protection of fingerprint data have also been designed. The studied approaches are multidisciplinary since their design and realization involved optical acquisition systems, multiple view geometry, image processing, pattern recognition, computational intelligence, statistics, and cryptography. The implemented biometric systems and algorithms have been applied to different biometric datasets describing a heterogeneous set of applicative scenarios. Results proved the feasibility of the studied approaches. In particular, the realized contactless biometric systems have been compared with traditional fingerprint recognition systems, obtaining positive results in terms of accuracy, usability, user acceptability, scalability, and security. Moreover, the developed techniques for the privacy protection of fingerprint biometric systems showed satisfactory performances in terms of security, accuracy, speed, and memory usage

Noisy iris segmentation with boundary regularization and reflections removal

The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I)

Neural-based iterative approach for iris detection in iris recognition systems

The detection of the iris boundaries is considered in the literature as one of the most critical steps in the identification task of the iris recognition systems. In this paper we present an iterative approach to the detection of the iris center and boundaries by using neural networks. The proposed algorithm starts by an initial random point in the input image, then it processes a set of local image properties in a circular region of interest searching for the peculiar transition patterns of the iris boundaries. A trained neural network processes the parameters associated to the extracted boundaries and it estimates the offsets in the vertical and horizontal axis with respect to the estimated center. The coordinates of the starting point are then updated with the processed offsets. The steps are then iterated for a fixed number of epochs, producing an iterative refinements of the coordinates of the pupils center and its boundaries. Experiments showed that the method is feasible and it can be exploited even in non-ideal operative condition of iris recognition biometric systems

Towards the prediction of renewable energy unbalance in smart grids

The production of renewable energy is increasing worldwide. To integrate renewable sources in electrical smart grids able to adapt to changes in power usage in heterogeneous local zones, it is necessary to accurately predict the power production that can be achieved from renewable energy sources. By using such predictions, it is possible to plan the power production from non-renewable energy plants to properly allocate the produced power and compensate possible unbalances. In particular, it is important to predict the unbalance between the power produced and the actual power intake at a local level (zones). In this paper, we propose a novel method for predicting the sign of the unbalance between the power produced by renewable sources and the power intake at the local level, considering zones composed of multiple power plants and with heterogeneous characteristics. The method uses a set of historical features and is based on Computational Intelligence techniques able to learn the relationship between historical data and the power unbalance in heterogeneous geographical regions. As a case study, we evaluated the proposed method using data collected by a player in the energy market over a period of seven months. In this preliminary study, we evaluated different configurations of the proposed method, achieving results considered as satisfactory by a player in the energy market

Neural-based quality measurement of fingerprint images in contactless biometric systems

Traditional fingerprint biometric systems capture the user fingerprint images by a contact-based sensor. Differently, contactless systems aim to capture the fingerprint images by an approach based on a vision system without the need of any contact of the user with the sensor. The user finger is placed in front of a special CCD-based system that captures the pattern of ridges and valleys of the fingertips. This approach is less constrained by the point of view of the user, but it requires much more capability of the system to deal with the focus of the moving target, the illumination problems and the complexity of the background in the captured image. During the acquisition procedure, the quality of each frame must be carefully evaluated in order to extract only the correct frames with valuable biometric information from the sequence. In this paper, we present a neural-based approach for the quality estimation of the contactless fingertips images. The application of the neural classification models allowed for a relevant reduction of the computational complexity permitting the application in realtime. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters

ALL-IDB : the acute lymphoblastic leukemia image database for image processing

The visual analysis of peripheral blood samples is an important test in the procedures for the diagnosis of leukemia. Automated systems based on artificial vision methods can speed up this operation and increase the accuracy and homogeneity of the response also in telemedicine applications. Unfortunately, there are not available public image datasets to test and compare such algorithms. In this paper, we propose a new public dataset of blood samples, specifically designed for the evaluation and the comparison of algorithms for segmentation and classification. For each image in the dataset, the classification of the cells is given, as well as a specific set of figures of merits to fairly compare the performances of different algorithms. This initiative aims to offer a new test tool to the image processing and pattern matching communities, direct to stimulating new studies in this important field of research

ECG biometric recognition : permanence analysis of QRS signals for 24 hours continuous authentication

Recent studies regard the use of ECG signals for biometric recognition exploiting the possibility of these signals to be frequently recorded for long time periods without any explicit actions performed by the users during the acquisitions. This aspect makes ECG signals particularly suitable for continuous authentication applications. In this context, researches have proved that the QRS complex is the most stable component of the ECG signal. In this paper, we perform a preliminary study on the persistency of QRS signals for continuous authentication systems. A recognition method based on multiple leads is proposed, and used to evaluate the persistency of the QRS complex in 24 hours Holter signals. This time interval can be considered as adequate for many possible applications in continuous authentication scenarios. The analysis is performed on a significantly large public Holter dataset and aims to search accurate matching and enrollment strategies for continuous authentication systems. At the best our knowledge, the results presented in this paper are based on the biggest set of ECG signals used to design continuous authentication applications in the literature. Results suggest that the QRS complex is stable only for a relatively small time period, and the performance of the proposed recognition method starts decreasing after two hours

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

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

Wildfire smoke detection using computational intelligence techniques enhanced with synthetic smoke plume generation

An early wildfire detection is essential in order to assess an effective response to emergencies and damages. In this paper, we propose a low-cost approach based on image processing and computational intelligence techniques, capable to adapt and identify wildfire smoke from heterogeneous sequences taken from a long distance. Since the collection of frame sequences can be difficult and expensive, we propose a virtual environment, based on a cellular model, for the computation of synthetic wildfire smoke sequences. The proposed detection method is tested on both real and simulated frame sequences. The results show that the proposed approach obtains accurate results

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%