A Novel Biometric System Based Hand Vein Recognition

Abstract

As a reliable and universal biometric characteristic, hand vein identification has attracted many interested researchers. The hand vein identification system exhibits several excellent advantages in the biometric domain because it meets the increasing demand of accuracy and robustness. In this paper, we propose a new biometric recognition system based on hand vein features. The detection and extraction of the region of interest is based on Voronoi Decomposition. Furthermore, contrast enhancement is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique. Starting with the basic Gaussian Matched Filter (GMF) and its variant, we propose a new technique called the Improved Gaussian Matched Filter method (IMPGMF) surmounting the false detection of hand vessels with the traditional GMF. Feature points are then detected based on ending and bifurcation structures in the image map obtained with the proposed IMPGMF method and taken as signature for our biometric system. Then, Artificial Neural Networks (ANN) are used for the classification step. In the validation step, we used a 1500 hand vein image from the BOSPHORUS database. The Error Equal Rate is 0.01 % and the Area Under curve of the corresponding system is approximately 0.98, demonstrating a very high security level.

Dynamic ROI extraction method for hand vein images

“…Most of researches on hand vein biometric have focused on ROI localisation, image enhancement [2], feature extraction [3–5], and multimodal recognition systems [6]. ROI localisation is a crucial process in hand vein identification or verification system.…”

“…All these factors may lead to inaccurate region of interest (ROI) detection and hence performance degradation of biometric system. Most of researches on hand vein biometric have focused on ROI localisation, image enhancement [2], feature extraction [3][4][5], and multimodal recognition systems [6]. ROI localisation is a crucial process in hand vein identification or verification system.…”

Dorsal Hand Vein Enhancement and Fake Vein Detection

“…To reduce the computation time and improve the calculation speed, the hand vein images are always low quality, so most of methods are preceded by a preprocessing step which can make it easier to extract vein features. Redhouane applied a double adaptive histogram equalization to enhance the contrast of the images [4]; Chanthamongkol used the contrast-limited adaptive histogram equalization in the enhancement of dorsal vein [5]; Guo Dan et al used multi-scale vessel enhancement filtering to enhance the noise-depressed images [6]; Trabelsi used contrast limited adaptive histogram equalization, moreover histogram equalization and adaptive histogram equalization algorithm are used to contrast with [7,8]; Maurício Ramalho used median and wiener filter [9]; Ramsoful used median and Gaussian filter [10]; Wei used Gaussian and high pass filter [11]; Kumar used Mexican Hat to enhance image. With a view to the importance of pre-processing, the guided filter [12] is proposed to enhance the hand vein images.…”

Biometria de veias: um método para extrair as informações discriminantes da imagem do dorso da mão

“…A variação do padrão das veias desta região entre os indivíduos permite autenticar uma pessoa com base nesta característica. Na literatura, diversos estudos têm sido realizados com base nas veias das mãos para a autenticação pessoal [7][8] [9] [10]. O intenso interesse por essaárea de pesquisaé justificado por algumas vantagens do uso das veias como tipo biométrico em relação aos demais (íris, impressão digital, face, entre outros), incluindo: alta variabilidade inter-classes e baixa variabilidade intra-classes; baixo custo para a montagem de um protótipo para aquisição de imagens das veias; sem contato físico entre o equipamento de aquisição e a mão; alto desempenho em termos de taxas de erro no processo de reconhecimento.…”