Showing papers by "Angelo Genovese published in 2013"

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

Abstract: 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 Enhanced With Synthetic Smoke Plume Generation

Abstract: 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.

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

Abstract: 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.

Privacy and Security in Environmental Monitoring Systems: Issues and Solutions

Abstract: Today there is an increasing interest in environmental monitoring for a variety of specific applications, with great impact especially on natural resource management and preservation, economy, and people’s life and health. Typical uses encompass, for example, Earth observation, meteorology, natural resource monitoring, agricultural and forest monitoring, pollution control, natural disaster observation and prediction, and critical infrastructure monitoring. While on one hand these systems play an important role in our society, on the other hand their adoption can raise a number of security and privacy concerns, which can present an obstacle for developing future environmental applications. In this chapter, we identify the main security and privacy issues characterizing the environmental data as well as the environmental monitoring infrastructures. We then provide an overview of possible countermeasures for diminishing the effects of these security and privacy issues.

A virtual environment for the simulation of 3D wood strands in multiple view systems for the particle size measurements

Abstract: In this paper, we present a complete virtual environment for the computation of synthetic three-dimensional samples representing free falling wood strands.The proposed method permits to simulate acquisitions performed by real multiple view setups in which the stream of strands falling out of a conveyor belt is analyzed with image processing techniques in order to compute the particle size distribution. Unfortunately, experiments in real time applications are complex and expensive, and the ground true is almost impossible to measure in such conditions. The creation of a metric and fully virtual environment of falling wood strands represent a key feature in order to properly design the illuminotecnic and optical setups, optimize the image processing methods as well as the three- dimensional reconstruction techniques, using controlled and fully repeatable virtual image datasets.

Chapter 57 – Privacy and Security in Environmental Monitoring Systems: Issues and Solutions

Abstract: There is an increasing interest in environmental monitoring for a variety of specific applications, with great impact especially on natural resource management and preservation, economy, and people's life and health. Typical uses encompass, for example, observation of Earth; meteorology; monitoring of natural resources, agriculture, and forestry; pollution control; observation and prediction of natural disasters; and monitoring of critical infrastructure. These systems have an important role in our society but their adoption can raise a number of security and privacy concerns that can present obstacles for the development of future environmental applications. In this chapter, we identify main security and privacy issues characterizing environmental data as well as environmental monitoring infrastructures. We then provide an overview of possible countermeasures to diminish the effects of these security and privacy issues.