Biometric verification/identification based on hands natural layout

Adán, Miguel; Adán, Antonio; Vázquez, Andrés S.; Torres, Roberto Augusto de Almeida

doi:10.1016/j.imavis.2007.08.010

Cited by 44 publications

(44 citation statements)

References 14 publications

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“…When it works as identifier, the system recognizes the identity of the person whose distance to the hyperplane is largest. If the system works as verifier, it accepts the identity of the person when the distance to the hyperplane is greater than the optimum threshold calculated in training step (Adán et al, 2008). Finally, the gamma value of the classifier is changed to get the best recognition rate.…”

Section: Properties Of the Hand Images Contained In The Databasementioning

confidence: 99%

2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification

Cebrián¹,

González²,

Ornedo³

2012

Wavelet Transforms and Their Recent Applications in Biology and Geoscience

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Section: Properties Of the Hand Images Contained In The Databasementioning

confidence: 99%

2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification

Cebrián¹,

González²,

Ornedo³

2012

Wavelet Transforms and Their Recent Applications in Biology and Geoscience

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“…Hand biometric systems have evolved from early approaches which considered flat-surface and pegs to guide the placement of the user's hand [2], [3], [4], to completely platform-free, non-contact techniques were user collaboration is almost not required [5], [6], [7], [8]. This development can be classified into three categories according to the image acquisition criteria [9]:…”

Section: Related Workmentioning

confidence: 99%

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

Section: Related Workmentioning

confidence: 99%

“…In other words, if arg i (min p(h k |H i ) ≥ th) determines that i = k then the sample h k is properly identified, otherwise the user is not enrolled in the system. Some approaches in literature fail in associating sample h k as a non-existing profile, since they provide the most likelihood an similar class, even if the sample provided by h k corresponds to a non-registered individual [26], [7].…”

Section: Template Matchingmentioning

confidence: 99%

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A Comparative Study on Unconstrained Hand Biometrics

Sierra

Casanova

Ávila

et al. 2011

2011 International Conference on Hand-Based Biometrics

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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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“…For these biometric systems, a precise and accurate image acquisition procedure is essential [1], [2], [3].…”

Section: Literature Reviewmentioning

confidence: 99%

Study of the effect of blurriness in image acquisition for hand biometrics in mobile devices

Santos

Ávila

Bailador

et al. 2011

2011 Carnahan Conference on Security Technology

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Abstract-This paper presents a study on the effect of blurred images in hand biometrics. Blurred images simulates out-of-focus effects in hand image acquisition, a common consequence of unconstrained, contact-less and platform-free hand biometrics in mobile devices.The proposed biometric system presents a hand image segmentation based on multiscale aggregation, a segmentation method invariant to different changes like noise or blurriness, together with an innovative feature extraction and a template creation, oriented to obtain an invariant performance against blurring effects.The results highlight that the proposed system is invariant to some low degrees of blurriness, requiring an image quality control to detect and correct those images with a high degree of blurriness. The evaluation has considered a synthetic database created based on a publicly available database with 120 individuals. In addition, several biometric techniques could benefit from the approach proposed in this paper, since blurriness is a very common effect in biometric techniques involving image acquisition.

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Biometric verification/identification based on hands natural layout

Cited by 44 publications

References 14 publications

2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification

2-D Discrete Wavelet Transform for Hand Palm Texture Biometric Identification and Verification

A Comparative Study on Unconstrained Hand Biometrics

Study of the effect of blurriness in image acquisition for hand biometrics in mobile devices

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