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Special Issue "Hand-Based Biometrics Sensors and Systems"

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A special issue of Sensors (ISSN 1424-8220).

Deadline for manuscript submissions: closed (31 January 2012)

Special Issue Editor

Guest Editor
Dr. Lei Zhang (Website)

Department of Computing, The Hong Kong Polytechnic University, Hong Hum, Kowloon, Hong Kong
Phone: 852-27667355
Interests: biometrics; image and video processing; pattern recognition; computer vision

Special Issue Information

Dear Colleagues,

Biometric technology has been attracting more and more attention from researchers and engineers of personal authentication, due to both the ever-growing demand on security and the competitive advantages of biometrics over traditional authentication technology. With the fast development of biometric sensors and algorithms, diverse biometric systems have been now deployed in various applications. Among these biometric technologies, the hand-based biometrics (including fingerprint, palm-print, hand geometry or hand shape, finger-knuckle print, hand vein, etc.) are most popular and have the largest (about 60%) shares in the biometrics market. However, there are still many challenging problems involved in improving the accuracy, robustness, efficiency, and user-friendliness of hand-based biometric systems, and new problems are also emerging with new applications, e.g. personal authentication on mobile devices and internet.

This special issue aims to introduce the recent progress of hand-based biometrics and timely address the challenges in designing, developing, and deploying modern hand-based biometrics sensors and systems for various practical applications. It will provide researchers and engineers an efficient way to share their latest research results, findings and experience in relevant fields. Specifically, it will help to identify new hand-based biometric technologies, new problems with hand-based biometrics, and new applications of hand-based biometrics.

The scope of this special issue, but not limited to, the following topics:

  • New sensors for hand-based biometric data acquisition
  • New system design for hand-based biometrics system
  • Preprocessing, indexing and recognition of fingerprint, palm-print, hand geometry (or hand shape), finger-knuckle-print, hand vein, and other hand-based biometrics
  • New and novel hand-based biometrics
  • Multi-modal hand-based biometrics
  • Securing and anti-spoofing techniques for hand-based biometrics
  • Touchless (or contact-free) and 3D hand-based biometrics
  • Applications of hand-based biometrics

Dr. Lei Zhang
Guest Editor

Keywords

  • hand-based biometric authentication
  • fingerprint; palmprint
  • hand geometry
  • finger-knuckle-print
  • hand vein
  • system design
  • data sensing
  • multi-modal biometrics

Published Papers (16 papers)

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Research

Open AccessArticle A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification
Sensors 2012, 12(7), 8691-8709; doi:10.3390/s120708691
Received: 17 May 2012 / Revised: 14 June 2012 / Accepted: 18 June 2012 / Published: 26 June 2012
PDF Full-text (1432 KB) | HTML Full-text | XML Full-text
Abstract
Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed [...] Read more.
Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Palmprint Recognition across Different Devices
Sensors 2012, 12(6), 7938-7964; doi:10.3390/s120607938
Received: 17 April 2012 / Revised: 21 May 2012 / Accepted: 22 May 2012 / Published: 8 June 2012
Cited by 5 | PDF Full-text (1615 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured [...] Read more.
In this paper, the problem of Palmprint Recognition Across Different Devices (PRADD) is investigated, which has not been well studied so far. Since there is no publicly available PRADD image database, we created a non-contact PRADD image database containing 12,000 grayscale captured from 100 subjects using three devices, i.e., one digital camera and two smart-phones. Due to the non-contact image acquisition used, rotation and scale changes between different images captured from a same palm are inevitable. We propose a robust method to calculate the palm width, which can be effectively used for scale normalization of palmprints. On this PRADD image database, we evaluate the recognition performance of three different methods, i.e., subspace learning method, correlation method, and orientation coding based method, respectively. Experiments results show that orientation coding based methods achieved promising recognition performance for PRADD. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Palmprint and Face Multi-Modal Biometric Recognition Based on SDA-GSVD and Its Kernelization
Sensors 2012, 12(5), 5551-5571; doi:10.3390/s120505551
Received: 1 March 2012 / Revised: 2 April 2012 / Accepted: 25 April 2012 / Published: 30 April 2012
Cited by 3 | PDF Full-text (824 KB) | HTML Full-text | XML Full-text
Abstract
When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s overall biometric data should be regarded as [...] Read more.
When extracting discriminative features from multimodal data, current methods rarely concern themselves with the data distribution. In this paper, we present an assumption that is consistent with the viewpoint of discrimination, that is, a person’s overall biometric data should be regarded as one class in the input space, and his different biometric data can form different Gaussians distributions, i.e., different subclasses. Hence, we propose a novel multimodal feature extraction and recognition approach based on subclass discriminant analysis (SDA). Specifically, one person’s different bio-data are treated as different subclasses of one class, and a transformed space is calculated, where the difference among subclasses belonging to different persons is maximized, and the difference within each subclass is minimized. Then, the obtained multimodal features are used for classification. Two solutions are presented to overcome the singularity problem encountered in calculation, which are using PCA preprocessing, and employing the generalized singular value decomposition (GSVD) technique, respectively. Further, we provide nonlinear extensions of SDA based multimodal feature extraction, that is, the feature fusion based on KPCA-SDA and KSDA-GSVD. In KPCA-SDA, we first apply Kernel PCA on each single modal before performing SDA. While in KSDA-GSVD, we directly perform Kernel SDA to fuse multimodal data by applying GSVD to avoid the singular problem. For simplicity two typical types of biometric data are considered in this paper, i.e., palmprint data and face data. Compared with several representative multimodal biometrics recognition methods, experimental results show that our approaches outperform related multimodal recognition methods and KSDA-GSVD achieves the best recognition performance. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Performance Evaluation of Fusing Protected Fingerprint Minutiae Templates on the Decision Level
Sensors 2012, 12(5), 5246-5272; doi:10.3390/s120505246
Received: 23 February 2012 / Revised: 11 April 2012 / Accepted: 16 April 2012 / Published: 26 April 2012
Cited by 4 | PDF Full-text (403 KB) | HTML Full-text | XML Full-text
Abstract
In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same [...] Read more.
In a biometric authentication system using protected templates, a pseudonymous identifier is the part of a protected template that can be directly compared. Each compared pair of pseudonymous identifiers results in a decision testing whether both identifiers are derived from the same biometric characteristic. Compared to an unprotected system, most existing biometric template protection methods cause to a certain extent degradation in biometric performance. Fusion is therefore a promising way to enhance the biometric performance in template-protected biometric systems. Compared to feature level fusion and score level fusion, decision level fusion has not only the least fusion complexity, but also the maximum interoperability across different biometric features, template protection and recognition algorithms, templates formats, and comparison score rules. However, performance improvement via decision level fusion is not obvious. It is influenced by both the dependency and the performance gap among the conducted tests for fusion. We investigate in this paper several fusion scenarios (multi-sample, multi-instance, multi-sensor, multi-algorithm, and their combinations) on the binary decision level, and evaluate their biometric performance and fusion efficiency on a multi-sensor fingerprint database with 71,994 samples. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Multispectral Palmprint Recognition Using a Quaternion Matrix
Sensors 2012, 12(4), 4633-4647; doi:10.3390/s120404633
Received: 20 February 2012 / Revised: 21 March 2012 / Accepted: 21 March 2012 / Published: 10 April 2012
Cited by 10 | PDF Full-text (770 KB) | HTML Full-text | XML Full-text
Abstract
Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different [...] Read more.
Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Scattering Removal for Finger-Vein Image Restoration
Sensors 2012, 12(3), 3627-3640; doi:10.3390/s120303627
Received: 7 February 2012 / Revised: 6 March 2012 / Accepted: 6 March 2012 / Published: 15 March 2012
Cited by 16 | PDF Full-text (2277 KB) | HTML Full-text | XML Full-text
Abstract
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the [...] Read more.
Finger-vein recognition has received increased attention recently. However, the finger-vein images are always captured in poor quality. This certainly makes finger-vein feature representation unreliable, and further impairs the accuracy of finger-vein recognition. In this paper, we first give an analysis of the intrinsic factors causing finger-vein image degradation, and then propose a simple but effective image restoration method based on scattering removal. To give a proper description of finger-vein image degradation, a biological optical model (BOM) specific to finger-vein imaging is proposed according to the principle of light propagation in biological tissues. Based on BOM, the light scattering component is sensibly estimated and properly removed for finger-vein image restoration. Finally, experimental results demonstrate that the proposed method is powerful in enhancing the finger-vein image contrast and in improving the finger-vein image matching accuracy. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Improving Fingerprint Verification Using Minutiae Triplets
Sensors 2012, 12(3), 3418-3437; doi:10.3390/s120303418
Received: 25 January 2012 / Revised: 28 February 2012 / Accepted: 28 February 2012 / Published: 8 March 2012
Cited by 10 | PDF Full-text (878 KB) | HTML Full-text | XML Full-text
Abstract
Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity [...] Read more.
Improving fingerprint matching algorithms is an active and important research area in fingerprint recognition. Algorithms based on minutia triplets, an important matcher family, present some drawbacks that impact their accuracy, such as dependency to the order of minutiae in the feature, insensitivity to the reflection of minutiae triplets, and insensitivity to the directions of the minutiae relative to the sides of the triangle. To alleviate these drawbacks, we introduce in this paper a novel fingerprint matching algorithm, named M3gl. This algorithm contains three components: a new feature representation containing clockwise-arranged minutiae without a central minutia, a new similarity measure that shifts the triplets to find the best minutiae correspondence, and a global matching procedure that selects the alignment by maximizing the amount of global matching minutiae. To make M3gl faster, it includes some optimizations to discard non-matching minutia triplets without comparing the whole representation. In comparison with six verification algorithms, M3gl achieves the highest accuracy in the lowest matching time, using FVC2002 and FVC2004 databases. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Two-Level Evaluation on Sensor Interoperability of Features in Fingerprint Image Segmentation
Sensors 2012, 12(3), 3186-3199; doi:10.3390/s120303186
Received: 9 February 2012 / Revised: 22 February 2012 / Accepted: 22 February 2012 / Published: 7 March 2012
Cited by 2 | PDF Full-text (612 KB) | HTML Full-text | XML Full-text
Abstract
Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment [...] Read more.
Features used in fingerprint segmentation significantly affect the segmentation performance. Various features exhibit different discriminating abilities on fingerprint images derived from different sensors. One feature which has better discriminating ability on images derived from a certain sensor may not adapt to segment images derived from other sensors. This degrades the segmentation performance. This paper empirically analyzes the sensor interoperability problem of segmentation feature, which refers to the feature’s ability to adapt to the raw fingerprints captured by different sensors. To address this issue, this paper presents a two-level feature evaluation method, including the first level feature evaluation based on segmentation error rate and the second level feature evaluation based on decision tree. The proposed method is performed on a number of fingerprint databases which are obtained from various sensors. Experimental results show that the proposed method can effectively evaluate the sensor interoperability of features, and the features with good evaluation results acquire better segmentation accuracies of images originating from different sensors. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Figures

Open AccessArticle Finger Vein Recognition Based on a Personalized Best Bit Map
Sensors 2012, 12(2), 1738-1757; doi:10.3390/s120201738
Received: 24 December 2011 / Revised: 2 February 2012 / Accepted: 3 February 2012 / Published: 9 February 2012
Cited by 33 | PDF Full-text (1038 KB) | HTML Full-text | XML Full-text
Abstract
Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and [...] Read more.
Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Figures

Open AccessArticle Embedded Palmprint Recognition System Using OMAP 3530
Sensors 2012, 12(2), 1482-1493; doi:10.3390/s120201482
Received: 4 January 2012 / Revised: 24 January 2012 / Accepted: 29 January 2012 / Published: 2 February 2012
Cited by 7 | PDF Full-text (438 KB) | HTML Full-text | XML Full-text
Abstract
We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce [...] Read more.
We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the ccentral pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle On the Feasibility of Interoperable Schemes in Hand Biometrics
Sensors 2012, 12(2), 1352-1382; doi:10.3390/s120201352
Received: 23 December 2011 / Revised: 14 January 2012 / Accepted: 18 January 2012 / Published: 1 February 2012
Cited by 6 | PDF Full-text (1136 KB) | HTML Full-text | XML Full-text
Abstract
Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability [...] Read more.
Personal recognition through hand-based biometrics has attracted the interest of many researchers in the last twenty years. A significant number of proposals based on different procedures and acquisition devices have been published in the literature. However, comparisons between devices and their interoperability have not been thoroughly studied. This paper tries to fill this gap by proposing procedures to improve the interoperability among different hand biometric schemes. The experiments were conducted on a database made up of 8,320 hand images acquired from six different hand biometric schemes, including a flat scanner, webcams at different wavelengths, high quality cameras, and contactless devices. Acquisitions on both sides of the hand were included. Our experiment includes four feature extraction methods which determine the best performance among the different scenarios for two of the most popular hand biometrics: hand shape and palm print. We propose smoothing techniques at the image and feature levels to reduce interdevice variability. Results suggest that comparative hand shape offers better performance in terms of interoperability than palm prints, but palm prints can be more effective when using similar sensors. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Figures

Open AccessArticle Transformation of Hand-Shape Features for a Biometric Identification Approach
Sensors 2012, 12(1), 987-1001; doi:10.3390/s120100987
Received: 28 November 2011 / Revised: 5 January 2012 / Accepted: 6 January 2012 / Published: 16 January 2012
Cited by 2 | PDF Full-text (390 KB) | HTML Full-text | XML Full-text
Abstract
The present work presents a biometric identification system for hand shape identification. The different contours have been coded based on angular descriptions forming a Markov chain descriptor. Discrete Hidden Markov Models (DHMM), each representing a target identification class, have been trained with [...] Read more.
The present work presents a biometric identification system for hand shape identification. The different contours have been coded based on angular descriptions forming a Markov chain descriptor. Discrete Hidden Markov Models (DHMM), each representing a target identification class, have been trained with such chains. Features have been calculated from a kernel based on the HMM parameter descriptors. Finally, supervised Support Vector Machines were used to classify parameters from the DHMM kernel. First, the system was modelled using 60 users to tune the DHMM and DHMM_kernel+SVM configuration parameters and finally, the system was checked with the whole database (GPDS database, 144 users with 10 samples per class). Our experiments have obtained similar results in both cases, demonstrating a scalable, stable and robust system. Our experiments have achieved an upper success rate of 99.87% for the GPDS database using three hand samples per class in training mode, and seven hand samples in test mode. Secondly, the authors have verified their algorithms using another independent and public database (the UST database). Our approach has reached 100% and 99.92% success for right and left hand, respectively; showing the robustness and independence of our algorithms. This success was found using as features the transformation of 100 points hand shape with our DHMM kernel, and as classifier Support Vector Machines with linear separating functions, with similar success. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle An Approach to Improve the Quality of Infrared Images of Vein-Patterns
Sensors 2011, 11(12), 11447-11463; doi:10.3390/s111211447
Received: 30 September 2011 / Revised: 23 November 2011 / Accepted: 29 November 2011 / Published: 1 December 2011
Cited by 2 | PDF Full-text (2918 KB) | HTML Full-text | XML Full-text
Abstract
This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach [...] Read more.
This study develops an approach to improve the quality of infrared (IR) images of vein-patterns, which usually have noise, low contrast, low brightness and small objects of interest, thus requiring preprocessing to improve their quality. The main characteristics of the proposed approach are that no prior knowledge about the IR image is necessary and no parameters must be preset. Two main goals are sought: impulse noise reduction and adaptive contrast enhancement technologies. In our study, a fast median-based filter (FMBF) is developed as a noise reduction method. It is based on an IR imaging mechanism to detect the noisy pixels and on a modified median-based filter to remove the noisy pixels in IR images. FMBF has the advantage of a low computation load. In addition, FMBF can retain reasonably good edges and texture information when the size of the filter window increases. The most important advantage is that the peak signal-to-noise ratio (PSNR) caused by FMBF is higher than the PSNR caused by the median filter. A hybrid cumulative histogram equalization (HCHE) is proposed for adaptive contrast enhancement. HCHE can automatically generate a hybrid cumulative histogram (HCH) based on two different pieces of information about the image histogram. HCHE can improve the enhancement effect on hot objects rather than background. The experimental results are addressed and demonstrate that the proposed approach is feasible for use as an effective and adaptive process for enhancing the quality of IR vein-pattern images. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Finger Vein Recognition Using Local Line Binary Pattern
Sensors 2011, 11(12), 11357-11371; doi:10.3390/s111211357
Received: 1 November 2011 / Revised: 28 November 2011 / Accepted: 29 November 2011 / Published: 30 November 2011
Cited by 44 | PDF Full-text (6283 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the [...] Read more.
In this paper, a personal verification method using finger vein is presented. Finger vein can be considered more secured compared to other hands based biometric traits such as fingerprint and palm print because the features are inside the human body. In the proposed method, a new texture descriptor called local line binary pattern (LLBP) is utilized as feature extraction technique. The neighbourhood shape in LLBP is a straight line, unlike in local binary pattern (LBP) which is a square shape. Experimental results show that the proposed method using LLBP has better performance than the previous methods using LBP and local derivative pattern (LDP). Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
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Open AccessArticle Gaussian Multiscale Aggregation Applied to Segmentation in Hand Biometrics
Sensors 2011, 11(12), 11141-11156; doi:10.3390/s111211141
Received: 14 October 2011 / Revised: 7 November 2011 / Accepted: 22 November 2011 / Published: 28 November 2011
Cited by 1 | PDF Full-text (4075 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The [...] Read more.
This paper presents an image segmentation algorithm based on Gaussian multiscale aggregation oriented to hand biometric applications. The method is able to isolate the hand from a wide variety of background textures such as carpets, fabric, glass, grass, soil or stones. The evaluation was carried out by using a publicly available synthetic database with 408,000 hand images in different backgrounds, comparing the performance in terms of accuracy and computational cost to two competitive segmentation methods existing in literature, namely Lossy Data Compression (LDC) and Normalized Cuts (NCuts). The results highlight that the proposed method outperforms current competitive segmentation methods with regard to computational cost, time performance, accuracy and memory usage. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)
Open AccessArticle Unconstrained and Contactless Hand Geometry Biometrics
Sensors 2011, 11(11), 10143-10164; doi:10.3390/s111110143
Received: 6 September 2011 / Revised: 14 October 2011 / Accepted: 14 October 2011 / Published: 25 October 2011
Cited by 10 | PDF Full-text (1198 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison [...] Read more.
This paper presents a hand biometric system for contact-less, platform-free scenarios, proposing innovative methods in feature extraction, template creation and template matching. The evaluation of the proposed method considers both the use of three contact-less publicly available hand databases, and the comparison of the performance to two competitive pattern recognition techniques existing in literature: namely Support Vector Machines (SVM) and k-Nearest Neighbour (k-NN). Results highlight the fact that the proposed method outcomes existing approaches in literature in terms of computational cost, accuracy in human identification, number of extracted features and number of samples for template creation. The proposed method is a suitable solution for human identification in contact-less scenarios based on hand biometrics, providing a feasible solution to devices with limited hardware requirements like mobile devices. Full article
(This article belongs to the Special Issue Hand-Based Biometrics Sensors and Systems)

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