Hand geometry identification system performance

Abstract-Biometrics applied to mobile devices are of great interest for security applications. Daily scenarios can benefit of a combination of both the most secure systems and most simple and extended devices.This document presents a hand biometric system oriented to mobile devices, proposing a non-intrusive, contact-less acquisition process where final users should take a picture of their hand in free-space with a mobile device without removals of rings, bracelets or watches.The main contribution of this paper is threefold: firstly, a feature extraction method is proposed, providing invariant hand measurements to previous changes; second contribution consists of providing a template creation based on hand geometric distances, requiring information from only one individual, without considering data from the rest of individuals within the database; finally, a proposal for template matching is proposed, minimizing the intra-class similarity and maximizing the inter-class likeliness. The proposed method is evaluated using three publicly available contact-less, platform-free databases. In addition, the results obtained with these databases will be compared to the results provided by two competitive pattern recognition techniques, namely Support Vector Machines (SVM) and k-Nearest Neighbour, often employed within the literature. Therefore, this approach provides an appropriate solution to adapt hand biometrics to mobile devices, with an accurate results and a non-intrusive acquisition procedure which increases the overall acceptance from the final user.

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“…Peg-free scenarios, although still requiring a platform to place the hand, like a scanner [7], [10].…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study on Unconstrained Hand Biometrics

Sierra

Casanova

Ávila

et al. 2011

2011 International Conference on Hand-Based Biometrics

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“…These features must describe and define the hand uniquely and univocally, and must remain invariant to changes of size, distance to camera, rotation and similar variations in acquisition. Some previous works provide similar templates based on width fingers and distances extracted from hand Boreki & Zimmer (2005); Sanchez-Reillo et al (2000), and others consider free-space acquisition Ferrer et al (2009);Zheng et al (2007), but without considering a high degree of freedom in hand changes and mobile devices acquisition. Before extracting features, tips and valleys are detected according to previous work de Santos Sierra et al (2009);Munoz et al (2010), based on the difference of pixels in the hand contour and hand centroid.…”

Section: Template Extractionmentioning

confidence: 99%

“…In addition, several color spaces have been also proposed to facilitate the procedure of segmentation, although most common used space is RGB Tan et al (2009). Several authors have proposed an infra-red illumination environment Ferrer et al (2009);Shahin et al (2008) based on the fact that infra-red illumination allows to extract hand contour easily since infra-red light highlights that region closer to the focus, and therefore, background is rarely illuminated. However, these acquisition systems require both a special illumination and an infra-red camera, difficult to be embedded on daily devices like mobiles and smartphones, for instance.…”

Section: Related Workmentioning

confidence: 99%

Hand Biometrics in Mobile Devices

Santos-Sierra¹,

Ávila²,

Guerra-Casanova³

et al. 2011

Advanced Biometric Technologies

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“…However, more challenging problems in segmentation appeared when hand biometrics required no constraint on background, presenting new trends in contact-less biometrics [3], [4], [5], [6].…”

Section: State Of the Artmentioning

confidence: 99%

“…An adequate solution for contact-less hand biometrics avoiding hard and time-consuming segmentation algorithms is based on infrared camera acquisition [4], [5], where the acquisition procedure facilitates a posterior hand extraction, given the fact that infrared illumination only highlights those objects closer to the sensor. In other words, infrared illumination avoids background acquisition.…”

Section: State Of the Artmentioning

confidence: 99%

Gaussian multiscale aggregation oriented to hand biometric segmentation in mobile devices

Sierra¹,

Ávila²,

Pozo³

et al. 2011

2011 Third World Congress on Nature and Biologically Inspired Computing

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Abstract-New trends in biometrics are oriented to mobile devices in order to increase the overall security in daily actions like bank account access, e-commerce or even document protection within the mobile.However, applying biometrics to mobile devices imply challenging aspects in biometric data acquisition, feature extraction or private data storage. Concretely, this paper attempts to deal with the problem of hand segmentation given a picture of the hand in an unknown background, requiring an accurate result in terms of hand isolation.For the sake of user acceptability, no restrictions are done on background, and therefore, hand images can be taken without any constraint, resulting segmentation in an exigent task.Multiscale aggregation strategies are proposed in order to solve this problem due to their accurate results in unconstrained and complicated scenarios, together with their properties in time performance. This method is evaluated with a public synthetic database with 480000 images considering different backgrounds and illumination environments.The results obtained in terms of accuracy and time performance highlight their capability of being a suitable solution for the problem of hand segmentation in contact-less environments, outperforming competitive methods in literature like Lossy Data Compression image segmentation (LDC).

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Hand geometry identification system performance

Cited by 19 publications

References 5 publications

A Comparative Study on Unconstrained Hand Biometrics

A Comparative Study on Unconstrained Hand Biometrics

Hand Biometrics in Mobile Devices

Gaussian multiscale aggregation oriented to hand biometric segmentation in mobile devices

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