Radiation optimization and image processing algorithms in the identification of hand vein patterns

Crisan, Septimiu; Tarnovan, Ioan Gavril; Crisan, Titus E.

doi:10.1016/j.csi.2009.11.008

Cited by 48 publications

(13 citation statements)

References 2 publications

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“…In fact, we have not yet optimised the program code, so it is possible that the computation time could be reduced. Among existing methods for dorsal hand vein recognition, those proposed by Lin and Fan, 8 Wang et al 11 and Crisan et al, 14 respectively, are the best known. Moreover, they characterise local details of the dorsal hand vein from different viewpoints, i.e.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…10, we can find that proposed method has the better performance, followed by the previous work, 15 The experimental results demonstrate that our current method is better than the geometric-based, appearancebased, patch-based and multiresolution representation methods, respectively. Generally, the geometric-based approach 14 extracts local features such as the locations and local statistics of the principal veins, minutiae points and ridge bifurcations. However, geometric features are difficult to be extracted, represented and compared while the discriminability of geometric features such as texture energy is not strong enough for dorsal hand vein recognition.…”

Section: Comparison and Discussionmentioning

confidence: 99%

“…Many verification technologies using biometric features of hand veins have been developed over the past decades. [5][6][7][8][9][10][11][12][13][14][15] Lin and Fan 8 presented personal verification results using palm dorsal images acquired from a thermal infrared (IR) camera operating in the 3?4-5?0 mm range. Their approach is based on the combination of multiresolution images obtained from pre-processed thermal vein images.…”

Section: Introductionmentioning

confidence: 99%

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Dorsal hand vein recognition based on 2D Gabor filters

Lee

Hsu

et al. 2013

The Imaging Science Journal

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Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. A reliable and robust personal verification approach using dorsal hand vein patterns is presented in this paper. This approach needs less computational and memory requirements and has a higher recognition accuracy than similar methods. In our work, a near-infrared charge-coupled device camera is adopted as an input device for capturing dorsal hand vein images, due to its advantages of the low-cost and non-contact imaging. In the proposed approach, two finger-peaks are automatically selected to define the region of interest in the dorsal hand vein images. In order to obtain effective pattern of dorsal hand vein vascular, we proposed an innovative and robust adaptive Gabor filter method to extract the dorsal hand vein patterns and encode the vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalised Hamming distance. A total of 6160 dorsal hand vein images were collected from 308 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (.99%) have been obtained by the proposed method, and the speed of the method (responding time ,0?8 s) is rapid enough for real-time recognition. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

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Section: Comparison and Discussionmentioning

confidence: 99%

Section: Comparison and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Dorsal hand vein recognition based on 2D Gabor filters

Lee

Hsu

et al. 2013

The Imaging Science Journal

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“…After a series of pre-processing: normalizing, filtering, segmenting, thresholding, smoothing, thinning, and pruning, the authors acquires clean and smooth vein pattern skeleton. Crisan et al [13] uses a new approach, where the vein detection process consists of an easy to implement device that takes a snapshot of the subject's veins under a source of near infrared radiation. Authors used the relative angles between vein segments, the number of end and bifurcation points, and the neighbors of each point for personal authentication.…”

Section: Related Workmentioning

confidence: 99%

Hand Vein Authentication System Using Dynamic ROI

Prasad

Kavati

Ravindra

2013

Communications in Computer and Information Science

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Abstract. This paper presents an efficient authentication system based on hand vein pattern. The stages involved in vein pattern authentication system are image acquisition, Region of Interest (ROI) Extraction, image enhancement, binarization, thinning, feature extraction and matching. We propose an algorithm for extraction of dynamic ROI from the hand vein image. The advantage of dynamic ROI extraction is that, ROI extracted for different hand images varies in size as the size of the hand varies and is possible to extract more features from a larger hand which otherwise is not possible with fixed ROI. A new thinning algorithm is used to extract one pixel thick medial axis vein network from the dynamic ROI and compared the results with matlab's thinning algorithm. The resulting thinned image may contain some artefacts, and we propose an algorithm to remove these artefacts. The minutiae features that represents the geometric information of the vein pattern is extracted which are bifurcation and ending points. Finally a matching algorithm is applied for authentication. The proposed system is efficient and got the lowest error rate.

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“…The important properties of infra-red imaging which makes it an appropriate choice for vein detection are (Crisan et al, 2010): a near infra-red (NIR) rays are capable of penetrating the biological tissue up to 3 cm b the reduced haemoglobin level in the venous blood absorbs more IR radiation as compared to the surrounding tissue.…”

Section: Palm Veinmentioning

confidence: 99%

Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application

Inamdar¹,

Dandawate²

2016

IJBM

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The authentication system used at the automated teller machines (ATM) is a unique personal identification number (PIN). This PIN can be easily tapped and misused. In this paper we propose a method in which the PIN is replaced by the biometrics of the individual to have a more secure transaction. A complete hardware system is designed to capture the biometric traits such as face, fingerprint and palm vein. The captured images are pre-processed and then features are extracted which are then fused at feature level. Cryptography is applied on the fused feature vector. Matching is performed using Euclidean distance at the server end. Palm vein is particularly chosen as a biometric trait along with widely used face and fingerprint because it is unique and is impossible to forge the vein pattern of an individual. Curvelet and Wavelet Transforms are used for the feature extraction. Experimental results indicate a good level of security and recognition rate of 91% and 89% is achieved on our own generated database. The results are promising when compared with other existing similar techniques.Reference to this paper should be made as follows: Inamdar, S.R. and Dandawate, Y.H. (2016) 'Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application', Int. He has 23 years of teaching experience and has published more than 55 papers 34 S.R. Inamdar and Y.H. Dandawate in reputed international and national conferences/referred journals. His areas of interest are in signal and image processing, embedded systems and soft computing. He is a reviewer and editorial board member of reputed journals in India and abroad. He is also working on several research projects. He is a senior member of IEEE and Fellow member of IETE, India.

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Radiation optimization and image processing algorithms in the identification of hand vein patterns

Cited by 48 publications

References 2 publications

Dorsal hand vein recognition based on 2D Gabor filters

Dorsal hand vein recognition based on 2D Gabor filters

Hand Vein Authentication System Using Dynamic ROI

Multimodal biometric cryptosystem based on fusion of wavelet and curvelet features in robust security application

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