コンピュータ・サイエンス : ACM computing surveys

Audio- and video-based biometric person authentication

Recent research has explored the possibility of extracting ancillary information from primary biometric traits viz., face, fingerprints, hand geometry, and iris. This ancillary information includes personal attributes, such as gender, age, ethnicity, hair color, height, weight, and so on. Such attributes are known as soft biometrics and have applications in surveillance and indexing biometric databases. These attributes can be used in a fusion framework to improve the matching accuracy of a primary biometric system (e.g., fusing face with gender information), or can be used to generate qualitative descriptions of an individual (e.g., young Asian female with dark eyes and brown hair). The latter is particularly useful in bridging the semantic gap between human and machine descriptions of the biometric data. In this paper, we provide an overview of soft biometrics and discuss some of the techniques that have been proposed to extract them from the image and the video data. We also introduce a taxonomy for organizing and classifying soft biometric attributes, and enumerate the strengths and limitations of these attributes in the context of an operational biometric system. Finally, we discuss open research problems in this field. This survey is intended for researchers and practitioners in the field of biometrics.

/pdf/what-else-does-your-biometric-data-reveal-a-survey-on-soft-1d8sy6v8ai.pdf

What Else Does Your Biometric Data Reveal? A Survey on Soft Biometrics

Thank you very much for reading handbook of biometrics. Maybe you have knowledge that, people have look numerous times for their favorite books like this handbook of biometrics, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some harmful virus inside their desktop computer. handbook of biometrics is available in our digital library an online access to it is set as public so you can download it instantly. Our books collection saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the handbook of biometrics is universally compatible with any devices to read.

Handbook Of Biometrics

Thank you very much for reading advanced digital signal processing and noise reduction. Maybe you have knowledge that, people have search hundreds times for their chosen books like this advanced digital signal processing and noise reduction, but end up in infectious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they are facing with some infectious bugs inside their laptop.

Advanced Digital Signal Processing And Noise Reduction

The detection of latent fingerprints can be a great aid in determining the authenticity of ancient artworks. In fact, a commonly used technique for the authentication of artworks consists in the comparison of fingerprints present on the surface of an artifact with other available latent fingerprints of an artist. This kind of analysis is particularly important for the authentication of clay artifacts. The clay, in fact, is often modeled barehanded, causing a great number of fingerprints left on the surface. In many cases, the artworks are very valuable or fragile and the latent fingerprints cannot be acquired using classical forensic methods. For this reason, contactless acquisition techniques have been proposed. Most of these techniques are based on the use of a single camera. In single-view acquisition systems, however, it can be difficult to properly estimate the size of the captured area, the obtained images can suffer from problems related to perspective distortions, and a calibration task cannot be performed in all the cases. In this paper, we propose a two-view acquisition system able to capture the latent fingerprints left on a clay artwork, and to compute their three-dimensional metric reconstruction. The obtained results show that the proposed approach is feasible and the reconstructed models provide a metric, view-independent, and less-distorted reconstruction of the fingerprint. In particular, we describe the application of the proposed method on a specific clay artwork associated by experts to the famous sculptor Antonio Canova (Italy, 1757–1822).

/pdf/two-view-contactless-fingerprint-acquisition-systems-a-case-2yj4yuslz7.pdf

Two-view contactless fingerprint acquisition systems: A case study for clay artworks

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.

/pdf/privacy-and-security-in-environmental-monitoring-systems-14yqnpqv8k.pdf

Privacy and Security in Environmental Monitoring Systems: Issues and Solutions

https://air.unimi.it/bitstream/2434/778842/2/imavis20.pdf

I-SOCIAL-DB: A labeled database of images collected from websites and social media for iris recognition

Touchless technologies for fingerphoto recognition based on smartphones can be considered selfie biometrics, in which a user captures images of his or her own biometric traits by using the integrated camera in a mobile device (here referred to as selfie fingerprint biometrics). Such systems mitigate the limitations of leaving latent fingerprints, dirt on the acquisition device released by the fingers, and skin deformations induced by touching an acquisition surface associated with a touch ID-based system. Furthermore, the use of the integrated camera to perform biometric acquisition bypasses the need of a dedicated fingerprint scanner. With respect to touch-based fingerprint recognition systems, selfie fingerprint biometrics require ad hoc methods for most steps of the recognition process. This is because the images captured using smartphone cameras present more complex backgrounds, lower visibility of the ridges, reflections, perspective distortions, and nonuniform resolutions. Selfie fingerprint biometric methods are usually less accurate than touch-based methods, but their performance can be satisfactory for a wide variety of security applications. This chapter presents a comprehensive literature review of selfie fingerprint biometrics. First, we introduce selfie fingerprint biometrics and touchless fingerprint recognition methods. Second, we describe the technological aspects of the different steps of the recognition process. Third, we analyze and compare the performances of recent methods proposed in the literature.

/pdf/a-scheme-for-fingerphoto-recognition-in-smartphones-50w3b8wqcj.pdf

A scheme for fingerphoto recognition in smartphones

Biometric systems based on touchless and less-constrained palmprint are being increasingly studied since they allow a favorable trade-off between high-accuracy and high usability recognition. Another advantage is that with a palmar hand acquisition, it is possible to extract the palmprint as well as the Inner Finger Texture (IFT) and increase the recognition accuracy without requiring further biometric acquisitions. Recently, most methods in the literature consider Deep Learning (DL) and Convolutional Neural Networks (CNN), due to their high recognition accuracy and the capability to adapt to biometric samples captured in heterogeneous and less-constrained conditions. However, current methods based on DL do not consider the fusion of palmprint with IFT. In this work, we propose the first novel method in the literature based on a CNN to perform the fusion of palmprint and IFT using a single hand acquisition. Our approach uses an innovative procedure based on training the same CNN topology separately on the palmprint and the IFT, adapting the neural model to the different biometric traits, and then performing a feature-level fusion. We validated the proposed methodology on a public database captured in touchless and less-constrained conditions, with results showing that the fusion enabled to increase the recognition accuracy, without requiring multlple biometric acquisltions.

/pdf/touchless-palmprint-and-finger-texture-recognition-a-deep-1qr87guuri.pdf

Angelo Genovese

Papers

Two-view contactless fingerprint acquisition systems: A case study for clay artworks

Privacy and Security in Environmental Monitoring Systems: Issues and Solutions

I-SOCIAL-DB: A labeled database of images collected from websites and social media for iris recognition

A scheme for fingerphoto recognition in smartphones

Touchless Palmprint and Finger Texture Recognition: A Deep Learning Fusion Approach