Low computational cost multilayer graph-based segmentation algorithms for hand recognition on mobile phones

Santos-Sierra, Daniel de; Arriaga-Gómez, Miguel F.; Bailador, Gonzalo; Ávila, Carmen Sánchez

doi:10.1109/ccst.2014.6986983

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Finally, hand recognition using low-cost devices is an important issue. For instance, Santos-Sierra et al 25 present an algorithm to segment hand images using multilayer graphs and Mostayed et al 26 use low resolution hand images and compute a set of position invariant features using the Radon transform.…”

Section: Related Workmentioning

confidence: 99%

A Contactless Identification System Based on Hand Shape Features

Bernardos

Sánchez

Portillo

et al. 2015

Procedia Computer Science

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This paper aims at studying the viability of setting up a contactless identification system based on hand features, with the objective of integrating this functionality as part of different services for smart spaces. The final identification solution will rely on a commercial 3D sensor (i.e. Leap Motion) for palm feature capture. To evaluate the significance of different hand features and the performance of different classification algorithms, 21 users have contributed to build a testing dataset. For each user, the morphology of each of his/her hands is gathered from 52 features, which include bones length and width, palm characteristics and relative distance relationships among fingers, palm center and wrist. In order to get consistent samples and guarantee the best performance for the device, the data collection system includes sweet spot control; this functionality guides the users to place the hand in the best position and orientation with respect to the device. The selected classification strategies -nearest neighbor, supported vector machine, multilayer perceptron, logistic regression and tree algorithms -have been evaluated through available Weka implementations. We have found that relative distances sketching the hand pose are more significant than pure morphological features. On this feature set, the highest correct classified instances (CCI) rate (>96%) is reached through the multilayer perceptron algorithm, although all the evaluated classifiers provide a CCI rate above 90%. Results also show how these algorithms perform when the number of users in the database change and their sensitivity to the number of training samples. Among the considered algorithms, there are different alternatives that are accurate enough for non-critical, immediate response applications.

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Section: Related Workmentioning

confidence: 99%

A Contactless Identification System Based on Hand Shape Features

Bernardos

Sánchez

Portillo

et al. 2015

Procedia Computer Science

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“…First of all, the hand is located within the image by haar cascades trained with open an closed hands. Then, the image is segmented by a multi-layer graph based segmentation algorithm [33] that delineates the hand's edges. Afterwards, fingers' widths are extracted at 20 different heights.…”

Section: Hand Geometrymentioning

confidence: 99%

“…First of all, the hand is located and segmented using the same algorithm used for hand geometry recognition [33], so the FTA and FTE are equal in both cases. Then, the biggest circle inscribed in the hand region whose center is the centroid of the hand is extracted and used as Region of Interest (ROI).…”

Section: Palmprintmentioning

confidence: 99%

gb2s μ MOD: A MUltiMODal biometric video database using visible and IR light

et al. 2016

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“…To facilitate the segmentation of images recorded at non‐controlled environments, it is possible to use infrared cameras [28, 29] but their use is not widespread yet. In this scenario, despite some authors keep using simple methods such as global thresholding [30] or skin colour modelling as a probability distribution [27, 31], more advanced techniques such as advanced graph‐based clustering techniques [32, 33] or neural networks [34] are usually required. Although without biometric purposes, other noteworthy methods including Markov Random Fields (MRFs) [35], Restricted Coulomb Energy neural networks [36, 37], shape models [9] and hybrid approaches such as the combination of MRF and shape priors [38] or a set of image‐based methods [39] have also been applied for hand image segmentation in non‐constrained images.…”

Section: Introductionmentioning

confidence: 99%

Flooding‐based segmentation for contactless hand biometrics oriented to mobile devices

et al. 2018

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Segmentation is a crucial stage in hand biometric recognition due to its direct influence on the feature extraction process. The actual trend toward contactless biometrics adds new challenges to traditional defiances, which are mainly related to the capturing conditions and the limitations on computational resources. Traditional methods do not succeed when variable capturing conditions are imposed and methods which are able to deal with daily-life situations are, in general, computationally expensive. In this study, a competitive flooding-based segmentation method oriented to mobile devices is proposed in order to achieve a compromised solution between accuracy and computational resources consumption. The method has been evaluated using images coming from five different databases which cover a wide spectrum of capturing conditions, one of them recorded as a part of this study. The results have been compared with other two well known segmentation techniques in terms of both accuracy and computation time.

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Low computational cost multilayer graph-based segmentation algorithms for hand recognition on mobile phones

Cited by 11 publications

References 13 publications

A Contactless Identification System Based on Hand Shape Features

A Contactless Identification System Based on Hand Shape Features

gb2s μ MOD: A MUltiMODal biometric video database using visible and IR light

Flooding‐based segmentation for contactless hand biometrics oriented to mobile devices

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