Hand Biometric Segmentation by Means of Fuzzy Multiscale Aggregation for Mobile Devices

Muñoz, Ángel G.; Sierra, Alberto de Santos; Ávila, Carmen Sánchez; Casanova, Javier Guerra; Pozo, Gonzalo Bailador del; Vera, Vicente

doi:10.1109/etchb.2010.5559293

Cited by 8 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…• Provide neighbour structure applying Delaunay triangulation Finally, this method comes out with precise and accurate results for hand segmentation [32]. The result section shows that the application of this method together with the feature extraction provides independency against blur effect.…”

Section: A Segmentationmentioning

confidence: 96%

“…The proposed segmentation algorithm is based on multiscale aggregation [31], [32]. Concretely, the method considers image I as a graph G = (V, E, W ), where nodes v i ∈ V correspond to pixels in image; edges e i,j ∈ E represent the union between two nodes v i and v j ; weights w i,j ∈ W describe the similarity between two nodes v i and v j associated by an edge e i,j .…”

Section: A Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

A Comparative Study on Unconstrained Hand Biometrics

Sierra

Casanova

Ávila

et al. 2011

2011 International Conference on Hand-Based Biometrics

View full text Add to dashboard Cite

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.

show abstract

Section: A Segmentationmentioning

confidence: 96%

Section: A Segmentationmentioning

confidence: 99%

A Comparative Study on Unconstrained Hand Biometrics

Sierra

Casanova

Ávila

et al. 2011

2011 International Conference on Hand-Based Biometrics

View full text Add to dashboard Cite

show abstract

“…At present, hand biometrics are oriented to mobile applications in order to provide more security to mobile devices [7], [8], with the single requirement of using the embedded camera within the mobilephone, involving no additional hardware. Therefore, more complicated algorithms are proposed given both the complexity of the segmentation aim and that mobile devices are increasing their capability to carry out complex operation, together with the fact that increasing acceptability by final user involves less restrictions in backgrounds.…”

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

Self Cite

View full text Add to dashboard Cite

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).

show abstract

“…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. The proposed method extracts features by dividing the finger from the basis to the tip in m parts.…”

Section: Template Extractionmentioning

confidence: 99%

“…Concerning segmentation evaluation, a supervised evaluation method Munoz et al (2010); Zhang et al (2008) was considered, comparing the segmentation result to a ground-truth solution obtained based on the segmentation carried out for first database. This first database contains hand acquisitions with a known background, becoming relatively easy to extract …”

Section: Segmentation Evaluationmentioning

confidence: 99%