Local feature approach to dorsal hand vein recognition by Centroid-based Circular Key-point Grid and fine-grained matching

Huang, Di; Zhang, Renke; Yin, Yuan; Wang, Yiding; Wang, Yunlong

doi:10.1016/j.imavis.2016.07.001

Cited by 34 publications

(12 citation statements)

References 13 publications

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“…The most important local texture-based approaches are different variants of locally binary patterns (LBP) or circular locally binary patterns (CLBP) [10] or Gabor texture descriptors [11]. Another type of local information extraction is based on local key point matching, for example SIFT (Scale Invariant Feature Transform) [12], Difference of Gaussian [13], Oriented Gradient Maps [14], Centroid-based Circular Key-point Grid (CCKG) [12].…”

Section: Related Workmentioning

confidence: 99%

CNN Approaches for Dorsal Hand Vein Based Identification

Lefkovits¹,

Lefkovits²,

Szilágyi³

2019

Computer Science Research Notes

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In this paper we present a dorsal hand vein recognition method based on convolutional neural networks (CNN). We implemented and compared two CNNs trained from end-to-end to the most important state-of-the-art deep learning architectures (AlexNet, VGG, ResNet and SqueezeNet). We applied the transfer learning and finetuning techniques for the purpose of dorsal hand vein-based identification. The experiments carried out studied the accuracy and training behaviour of these network architectures. The system was trained and evaluated on the best-known database in this field, the NCUT, which contains low resolution, low contrast images. Therefore, different pre-processing steps were required, leading us to investigate the influence of a series of image quality enhancement methods such as Gaussian smoothing, inhomogeneity correction, contrast limited adaptive histogram equalization, ordinal image encoding, and coarse vein segmentation based on geometrical considerations. The results show high recognition accuracy for almost every such CNN-based setup.

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Section: Related Workmentioning

confidence: 99%

CNN Approaches for Dorsal Hand Vein Based Identification

Lefkovits¹,

Lefkovits²,

Szilágyi³

2019

Computer Science Research Notes

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“…Palmprint biometrics, with the center of the palm not touching the capture device for identification, is less likely to be copied by others and is more hygienic [9]. Contrary to palmprint biometrics, hand vein biometrics, such as dorsal hand veins [10][11][12] and palm veins [13,14], are studied in security with advantages in liveness detection and anti-spoofing [12]. In the meantime, hyperspectral technology known from remote sensing [15][16][17][18], has been introduced in biometrics, where it is applied in high-security scenarios [18].…”

Section: Introductionmentioning

confidence: 99%

“…The dorsal hand mainly concentrates on the vein structures, first discovered by Joe Rice, a senior Kodak engineer when he was designing an infrared barcode system [26]. Huang et al [12] performed dorsal hand vein recognition based on an 850 nm NIR dorsal hand database, while other databases with the same band can be found in [27][28][29]. Chen et al [30] applied hyperspectral techniques to select the best spectrum for the improvement of dorsal hand vein recognition, which showed potential in hyperspectral hand biometrics.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Nie¹,

Zhang²,

Zhao³

2019

Applied Sciences

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Image acutance or edge contrast in an image plays a crucial role in hyperspectral hand biometrics, especially in the local feature representation phase. However, the study of acutance in this application has not received a lot of attention. Therefore, in this paper we propose that there is an optimal range of image acutance in hyperspectral hand biometrics. To locate this optimal range, a thresholded pixel-wise acutance value (TPAV) is firstly proposed to assess image acutance. Then, through convolving with Gaussian filters, a hyperspectral hand image was preprocessed to obtain different TPAVs. Afterwards, based on local feature representation, the nearest neighbor method was used for matching. The experiments were conducted on hyperspectral dorsal hand vein (HDHV) and hyperspectral palm vein (HPV) databases containing 53 bands. The results that achieved the best performance were those where image acutance was adjusted to the optimal range. On average, the samples with adjusted acutance compared to the original improved by a recognition rate (RR) of 29.5% and 45.7% for the HDHV and HPV datasets, respectively. Furthermore, our method was validated on the PolyU multispectral palm print database producing similar results to that of the hyperspectral. From this we can conclude that image acutance plays an important role in hyperspectral hand biometrics.

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“…To explore various applications from border management, ecommerce, online banking to healthcare services, we need to explore strong feature extraction techniques. Over the past years, mainly two paradigm of features extraction methodologies were explored: vein pattern segmentation [2][3][4][5][6][7] and texture feature extraction from vein images [8]. To our view, the later could give more generalized representation of vein characteristics, which can use information of veins and adjacent tissues.…”

Section: Introductionmentioning

confidence: 99%

Texture Based Vein Biometrics for Human Identification: A Comparative Study

Bashar¹,

Murshed

2018

2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC)

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Hand vein biometric is an important modality for human authentication and liveness detection in many applications. Reliable feature extraction is vital to any biometric system. Over the past years, two major categories of vein features, namely vein structures and vein image textures, were proposed for hand dorsal vein based biometric identification. Of them, texture features seem important as it can combine skin microtextures along with vein properties. In this study, we have performed a comparative study to identify potential texture features and feature-classifier combination that produce efficient vein biometric systems. Seven texture features (HOG, GABOR, GLCM, SSF, DWT, WPT, and LBP) and three multiclass classifiers (LDA, ESVM, and KNN) were explored towards the supervised identification of human from vein images. An experiment with 400 infrared (IR) hand images from 40 adults indicates the superior performance of the histogram of oriented gradients (HOG) and simple local statistical feature (SSF) with LDA and ESVM classifiers in terms of average accuracy (> 90%), average Fscore (> 58%) and average specificity (>93%). The decision-level fusion of the LDA and ESVM classifier with single texture features showed improved performances (by 2.2 to 13.2% of average Fscore) over individual classifier for human identification with IR hand vein images.

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Local feature approach to dorsal hand vein recognition by Centroid-based Circular Key-point Grid and fine-grained matching

Cited by 34 publications

References 13 publications

CNN Approaches for Dorsal Hand Vein Based Identification

CNN Approaches for Dorsal Hand Vein Based Identification

Discriminative Local Feature for Hyperspectral Hand Biometrics by Adjusting Image Acutance

Texture Based Vein Biometrics for Human Identification: A Comparative Study

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