Ruggero Donida Labati

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.

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).

A privacy-compliant fingerprint recognition system based on homomorphic encryption and Fingercode templates

The privacy protection of the biometric data is an important research topic, especially in the case of distributed biometric systems. In this scenario, it is very important to guarantee that biometric data cannot be steeled by anyone, and that the biometric clients are unable to gather any information different from the single user verification/identification. In a biométrie system with high level of privacy compliance, also the server that processes the biométrie matching should not learn anything on the database and it should be impossible for the server to exploit the resulting matching values in order to extract any knowledge about the user presence or behavior. Within this conceptual framework, in this paper we propose a novel complete demonstrator based on a distributed biométrie system that is capable to protect the privacy of the individuals by exploiting cryptosystems. The implemented system computes the matching task in the encrypted domain by exploiting homomorphic encryption and using Fingercode templates. The paper describes the design methodology of the demonstrator and the obtained results. The demonstrator has been fully implemented and tested in real applicative conditions. Experimental results show that this method is feasible in the cases where the privacy of the data is more important than the accuracy of the system and the obtained computational time is satisfactory.

Agent-based image iris segmentation and multiple views boundary refining

The paper presents two different methods to deal with the problem of iris segmentation: an agent-based method capable to localize the center of the pupil and a method to process the iris boundaries by a multiple views approach. In the first method, an agent corresponds to the coordinates of a specific point of analysis in the input image. A population of agents is deployed in the input image, then, each agent collects local information concerning the intensity patterns visible in its region of interest. By iterations, an agent changes its position accordingly to the local properties, moving towards the estimation of the pupil center. If no available information is present in its region of interest, the agent will move itself along a random walk. After few iterations, the population tends to spread and then concentrate in the inner portion of the pupil. Once the center of the pupil has been located, the inner and outer iris boundaries are refined by an approach based on multiple views analysis. This method starts with a set of points that can be considered as an approximation of the pupil center. For each point, a detailed estimation of the iris boundaries is computed, and the final description of the iris boundaries is obtained by merging all the obtained descriptions. The two methods were tested using CASIA v.3 and UBIRIS v.2 images. Experiments show that the proposed approaches are feasible, also in eye images taken in noisy or non-ideal conditions, achieving a total error segmentation accuracy up to 97%.

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%.

Wildfire smoke detection using computational intelligence techniques

In this paper, we propose an image processing system for the detection of wildfire smoke based on computational intelligence techniques and capable of adapting to different applicative environments. The proposed system is designed for processing with limited computational complexity. The detection process focuses on the extraction of specific features of wildfire smoke. A computational intelligence classifier is adopted to identify the presence of smoke. In order to test its effectiveness, the proposed system has been tested with low quality frame sequences, providing the capability to deal also with low cost cameras. The results indicate that the proposed approach is accurate and can be effectively applied in different environmental conditions.

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.

A preliminary study on continuous authentication methods for photoplethysmographic biometrics

Recent studies in biometrics focus on one dimensional physiological signals commonly acquired in medical applications, like electrocardiogram (ECG), electroencephalograms (EEG), phonocardiogram (PCG), and photoplethysmogram (PPG). In this context, an important application is in continuous authentication scenarios since physiological signals are frequently captured for long time periods in order to monitor the health status of the patients.

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 real-time. Experimental results show that the proposed method has an adequate accuracy, and it can capture fingerprints at a distance up to 0.2 meters.