Representation of Hand Dorsal Vein Features Using a Low Dimensional Representation Integrating Cholesky Decomposition

Khan, Maleika Heenaye-Mamode; Subramanian, R. K.; Khan, Naushad Ali Mamode

doi:10.1109/cisp.2009.5304218

Cited by 18 publications

(3 citation statements)

References 12 publications

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“…Morphological operations on local features for dorsal vein recognition have also been examined [17]- [19]. Integration of Cholesky decomposition with low dimensional representation for dorsal vein feature representation was done in [20] while the use of Linear Discriminant Analysis (LDA) on dorsal vein images was proposed in [21].…”

Section: Literature Reviewmentioning

confidence: 99%

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

2020

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Multimodal biometric systems are preferred as a defense compared to unimodal systems. This study introduces an open access multimodal vein database named FYO with each letter dedicated to each author's name. The database involves three biometric traits; palm vein, dorsal vein and wrist vein of the same individuals, to explore and enhance research in the area of using these traits to create a spoof-proof multimodal authentication system. The vein images of FYO are acquired using medical vein finder in a controlled environment. Comparisons are performed to show the differences with the existing well known databases and the state-of-the-art recognition algorithms. Hand-crafted feature extractors such as Binarized Statistical Image Features (BSIF), Gabor filter and Histogram of Oriented Gradients (HOG) are applied to show the viability of the vein datasets. Additionally, a deep learning based Convolutional Neural Networks (CNN) architecture is proposed with two models using decision-level fusion of palmar, dorsal and wrist biometric traits on vein images. Unimodal systems, multimodal systems and the proposed architecture are tested on several vein datasets including palmar, dorsal and wrist vein images. Experimental results based on accuracy and computation time on our FYO datasets showed competitive output with that of other databases such as Tongji Contactless Palm Vein database, VERA, PUT, Badawi and Bosphorus hand vein databases. Moreover, the proposed CNN architecture on three vein biometric traits show superior performance compared to hand-crafted methods. INDEX TERMS Data fusion, deep learning, hand-crafted features, vein recognition, multimodal biometrics.

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Section: Literature Reviewmentioning

confidence: 99%

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

2020

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“…In last decades, there exist increasing amount of research works focusing on hand vein recognition using the vein pattern in the palm part [3]- [5], the back of the hand [6]- [8], or fingers [9]. Although there have been already several attempts on hand vein recognition by adopting holistic techniques, e.g., principal component analysis (PCA) [10], linear discriminant analysis (LDA) [11], etc., the changes of viewpoint, lighting intensity, distortion, and occlusion largely imped their development. In contrast, local feature based approaches become dominant due to its robustness to the aforementioned disturbing factors.…”

Section: Introductionmentioning

confidence: 99%

Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints

Huang

Tang

Wang

et al. 2015

IEEE Trans. Cybern.

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As an emerging biometric for people identification, the dorsal hand vein has received increasing attention in recent years due to the properties of being universal, unique, permanent, and contactless, and especially its simplicity of liveness detection and difficulty of forging. However, the dorsal hand vein is usually captured by near-infrared (NIR) sensors and the resulting image is of low contrast and shows a very sparse subcutaneous vascular network. Therefore, it does not offer sufficient distinctiveness in recognition particularly in the presence of large population. This paper proposes a novel approach to hand-dorsa vein recognition through matching local features of multiple sources. In contrast to current studies only concentrating on the hand vein network, we also make use of person dependent optical characteristics of the skin and subcutaneous tissue revealed by NIR hand-dorsa images and encode geometrical attributes of their landscapes, e.g., ridges, valleys, etc., through different quantities, such as cornerness and blobness, closely related to differential geometry. Specifically, the proposed method adopts an effective keypoint detection strategy to localize features on dorsal hand images, where the speciality of absorption and scattering of the entire dorsal hand is modeled as a combination of multiple (first-, second-, and third-) order gradients. These features comprehensively describe the discriminative clues of each dorsal hand. This method further robustly associates the corresponding keypoints between gallery and probe samples, and finally predicts the identity. Evaluated by extensive experiments, the proposed method achieves the best performance so far known on the North China University of Technology (NCUT) Part A dataset, showing its effectiveness. Additional results on NCUT Part B illustrate its generalization ability and robustness to low quality data.

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“…The wide range usage of these techniques in commercial and law enforcement applications, such as biometric authentication [7], video surveillance [39] and information security [1] has made it a popular and significant area of research. Nowadays, we are capable to identify people through automatic recognition techniques based on physiological and characteristic behaviours, such as finger print [28,29], iris [2,3,4], ear [5], vein face [27], and so on. Although many techniques have been proposed for pattern recognition [30], especially face recognition [31,32,33], and a number of studies [34,35,36] have been performed in the literature to evaluate and compare the existing techniques, there is still a long road ahead to achieve optimal approaches.…”

Section: Introductionmentioning

confidence: 99%

Experiments with Face Recognition Using a Novel Approach Based on CVQ Technique

Mehrbakhsh

Khalilian

2012

IFIP Advances in Information and Communication Technology

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Face recognition techniques attempt to identify faces according to the patterns of mouth, lip, eyes and so on. However, the effectiveness of existing approaches degrades in presence of uncontrolled conditions such as variations of background light and image sizes. To deal with this problem, we propose a novel approach based on Classified Vector Quantization (CVQ) technique. The new approach divides images into some blocks and each block is classified into several patterns. Then, the Vector Quantization (VQ) technique is applied on the vectors of each pattern. In order to evaluate our approach, we have conducted a family of experiments on some standard image databases, MIT, YALE, and AR. The results demonstrate that the new approach is steadily capable of identifying faces in different situations.

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Representation of Hand Dorsal Vein Features Using a Low Dimensional Representation Integrating Cholesky Decomposition

Cited by 18 publications

References 12 publications

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

FYO: A Novel Multimodal Vein Database With Palmar, Dorsal and Wrist Biometrics

Hand-Dorsa Vein Recognition by Matching Local Features of Multisource Keypoints

Experiments with Face Recognition Using a Novel Approach Based on CVQ Technique

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