Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

Belean, Bogdan; Streza, M.; Crisan, Septimiu; Emerich, Simina

doi:10.24846/v26i3y201706

Cited by 17 publications

(12 citation statements)

References 16 publications

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“…The experimental results on a data set of 100 images were promising. Belean et al [14] extracted vein vessel structures corresponding to hand image samples of the same person and trained multi-layer perceptron neural network to classify them. In 2018, based on minutiae features from skeleton images, Chuang [15] proposed a local feature-based vein representation method to learn the most discriminative regions and features for identification.…”

Section: Related Workmentioning

confidence: 99%

Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching

2018

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Dorsal hand vein recognition is a kind of biometric technique that has emerged in the last two decades. Owing to its safety, accuracy, and effectiveness, more and more researchers are involved in the study. Here, the authors presented a dorsal hand vein recognition system under uncontrolled environments based on biometric graph matching (BGM). Firstly, the authors establish two hand vein databases under natural indoor lighting conditions, i.e. XJTU-A and XJTU-B, with the hand not fixed. Secondly, the authors focus on optimising the image preprocessing steps in terms of region of interest (ROI) extraction, vein segmentation, and vein skeleton extraction. An 'open' operation with a large parameter is carried out to make the ROI extraction more abundant based on the maximum inscribed circle. In vein segmentation, the authors use the curvature point algorithm to better extract the vein skeleton. Thirdly, BGM algorithm is adopted to obtain distance measurements. The authors use single distance measure and multiple distance measures to obtain the threshold for recognition, respectively. Finally, the proposed dorsal hand vein recognition system is tested in three databases, and experiment results show that the improvement of the entire algorithms leads to high accuracy and strong robustness of the recognition system, whether under uncontrolled or controlled conditions.

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Section: Related Workmentioning

confidence: 99%

Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching

2018

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“…In the last decades, face and facial expressions recognition has received crucial attention from the worldwide research community, and it is a popular scope in data and object recognition and computer application according to its enormous and motivated range of security and criminal applications such as forensic face recognition [1][2][3], biometric authentication [4,5], video surveillance [6], information security [7] and edge detection [8]. Face recognition system is mostly used to perform verification of human identity, this way is based on feature extraction and dimensionality reduction, and a number of facial recognition systems have been produced with distinct measure of success.…”

Section: Introductionmentioning

confidence: 99%

Face recognition using fractional coefficients and discrete cosine transform tool

Moussa

Hmila

Douik

2021

IJECE

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Face recognition is a computer vision application based on biometric information for automatic person identification or verification from image sequence or a video frame. In this context DCT is the easy technique to determine significant parameters. Until now the main object is selection of the coefficients to obtain the best recognition. Many techniques rely on premasking windows to discard the high and low coefficients to enhance performance. However, the problem resides in the shape and size of premask. To improve discriminator ability in discrete cosine transform domain, we used fractional coefficients of the transformed images with discrete cosine transform to limit the coefficients area for a better performance system. Then from the selected bands, we use the discrimination power analysis to search for the coefficients having the highest power to discriminate different classes from each other. Feature selection algorithm is a key issue in all pattern recognition system, in fact this algorithm is utilized to define features vector among several ones, where these features are selected according a specified discrimination criterion. Many classifiers are used to evaluate our approach like, support vector machine and random forests. The proposed approach is validated with Yale and ORL Face databases. Experimental results prove the sufficiency of this method in face and facial expression recognition field.

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“…G. H. Liu et al [12] present image retrieval technique based on color difference histogram (CDH) to extract color features coded in * * * . Color information is also used in steganography field [13,14] and Biometric Authentication [15,16], for instance S. Hemalatha et al [17] propose a new image steganography technique to conceal secret information in color image, given the unsatisfactory results with the grey scale images, in fact color image can hold a big amount of secret information for this reason they have used two color spaces RGB and YCbCr, experimental results show that YCbCr doesn't requires enough computing time than the RGB one, and is easy for extracting secret images. S. Chitra et al [18] propose a technique in biometric field that consists to face recognition based on skin color detection, the first step of this algorithm convert original image in HSV and YCbCr color space then collect the value of H, S, Cb, Crand finally check whether these values are satisfied with the specified threshold values.…”

Section: Introductionmentioning

confidence: 99%

An iterative algorithm for color space optimization on image segmentation

Moussa¹

2021

TELKOMNIKA

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This paper proposes, a novel hybrid color component (HCC) issued from amounts number of color space with iterative manner, in fact traditional images obtained by RGB sensor weren't the effective way in image processing applications, for this purpose we have propose a supervised algorithm to substitute RGB level by hybrid and suitable color space at the aim to make well representation of the handled amounts of data, this step is extremely important because the obtained results it will be injected in many future studies like tracking, classification, steganography and cryptography. The second part of this paper consists to segment image coded in hybrid color space already selected, the used algorithm is inspired from kernel function where statistical distribution was used to model background and Bayes rule to make decision of the membership of each pixel, in this research topics we have extended this algorithm in the aim to improve compactness of these distribution. Cauchy background modeling and subtraction is used, and shows the high accuracy of automatic player detection.

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Dorsal Hand Vein Pattern Analysis and Neural Networks for Biometric Authentication

Cited by 17 publications

References 16 publications

Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching

Towards application of dorsal hand vein recognition under uncontrolled environment based on biometric graph matching

Face recognition using fractional coefficients and discrete cosine transform tool

An iterative algorithm for color space optimization on image segmentation

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