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Impact of NIR wavelength lighting in image acquisition on finger vein biometric system effectiveness

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Finger vein biometric systems become increasingly more popular because they offer higher security comparing to other authentication solutions with respect to positive persons experience. Those systems operate on near infrared light (NIR) in wavelength range from around 700 to 1000 nm, however dedicated research to determine impact of NIR lighting on biometric system effectiveness has not been conducted and presented in the literature ever before. In this paper the study of correlation between wavelengths in NIR spectra and effectiveness of person identification in a biometric system is presented. To achieve that goal, a new model of image acquisition system allowing change of light wavelengths has been created and NIR finger vein dataset containing 11 556 images was established. Furthermore, this model was used to perform experimental work and proof that some NIR wavelengths better suit for vein patterns acquisition, allowing to increase the recognition effectiveness of finger vein biometric systems.
Twórcy
autor
  • Silesian University of Technology, Faculty of Automatic Control, Electronics and Computer Science, ul. Akademicka 16, 44-100 Gliwice, Poland
autor
  • Silesian University of Technology, Institute of Electronics, Akademicka 16, 44-100, Gliwice, Poland
autor
  • Silesian University of Technology, Institute of Electronics, Akademicka 16, 44-100, Gliwice, Poland
Bibliografia
  • [1] J. Hashimoto, Finger vein authentication technology and its future, IEEE Symposium on VLSI Circuits Digest of Technical Paper (2008) 5–8, Honolulu, Hawaii, June.
  • [2] D. Hartung, C. Busch, Biometrische Fingererkennung - Fusion von Fingerabdruck, Fingervenen -und Fingergelenkbild, 12 Deutscher IT - Sicherheitskongress des BSI, 10–12, Sicher in die digitale Welt von morgen, Berlin, Germany, 2011, pp. 1-21, May (IN GERMAN).
  • [3] A. Hoshyan, R. Sulaiman, A. Houshyar, Smart Access Control with Finger Vein Authentication and Neural Network, J. American Science 7 (9) (2011) 192–200.
  • [4] Finger Vein USM.(FV-USM) Database, Available from: http://blog.eng.usm.my/fendi/?pageid=262 (Accessed: 08.03.2017).
  • [5] SDUMLA-HMT Database, Available from: http://mla.sdu.edu.cn/sdumla-hmt.html (Accessed: 08.03.2017).
  • [6] The Hong Kong Polytechnic University Finger Image Database, (Version 1.0). Available from: http://www4.comp.polyu.edu.hk/∼csajaykr/fvdatabase.htm (Accessed: 08.03.2017).
  • [7] J. Kim, et al., Non-contact finger vein acquisition system using NIR laser Sensors, Cameras, and Systems for Industrial/Scientific Applications X, Proc. SPIE (2009), 72490Y-1−72490Y-8.
  • [8] L. Chen, Z. Li, Y. Wu, L. Feng, A Nonlinear Diffusion Filter Model to Enhance Infrared Multi-Wave-Band Finger Vein Images, International Industrial Informatics and Computer Engineering Conference (2015).
  • [9] D. Yin, Z. Ding, Research on Finger Vein Acquisition Based on Wavelength Choice, International Symposium on Computers & Informatics (2015).
  • [10] PiNoIR, Infrared camera module for Raspberry Pi, Available from: http://www.raspberrypi-spy.co.uk/2013/10/pi-noir-infrared-camera-module-for-raspberry-pi/ (Accessed 20.06.2014).
  • [11] M. Waluś, K. Bernacki, A. Popowicz, Quality assessment of NIR finger vascular images for exposure parameter optimization, Biomedical Res. 2 (2016) 383–391.
  • [12] M. Waluś, Rozpoznawanie osób na podstawie układu naczyniowego palców dłoni, PhD thesis, 2016 (IN POLISH) Gliwice, Poland.
  • [13] N. Miura, A. Nagasaka, T. Miyatake, Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification, Mach. Vision Appl. 15 (2004) 194–203.
  • [14] R. Xiao, G. Yang, Y. Yin, L. Yang, A Novel Matching Strategy for Finger Vein Recognition, Lect. Notes Comput. Sci. 7751 (2013) 364–371.
  • [15] M.P. Dubuisson, A.K. Jain, A modified Hausdorff distance for object matching, in: Proc. Int. Conf. Pattern Recogn., Jerusalem, Israel, 1994, pp. 566–568.
  • [16] E.C. Lee, H. Jung, D. Kim, New finger biometric method using near infrared imaging, Sensors 11 (2011) 2319–2333.
  • [17] B. Sankur, M. Sezgin, A Survay Over Image Thresholding Techniques and Quantitative Performance Evaluation, J. Electron. Imaging 13 (2004) 146–165.
  • [18] N. Miura, A. Nagasaka, T. Miyatake, Feature extraction of finger-vein patterns based on repeated line tracking and its application to personal identification, Mach. Vision Appl. 15 (2004) 194–203.
  • [19] N. Miura, A. Nagasaka, T. Miyatake, Extraction of finger-vein patterns using maximum curvature points in image profiles, IEICE Transactions on Information and Systems 90 (2007) 1185–1194.
  • [20] L. Liu, D. Zhang, J. You, Detecting wide lines using isotropic nonlinear filtering, IEEE T. Image Process. 16 (2007) 1584–1595.
  • [21] J. Hong, G. Shuxu, L. Xueyan, Q. Xiaohua, Vein pattern extraction based on the position-gray-profile curve, 2nd International Congress on Image and Signal Processing CISP’09 (2009) 1–4.
  • [22] T. Ojala, M. Pietikäinen, T. Mäenpää, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE T. Pattern Anal. 24 (2002) 971–987.
  • [23] B.J. Kang, K.R. Park, J.H. Yoo, J.N. Kim, Multimodal biometric method that combines veins, prints, and shape of a finger, Opt. Eng. 50 (2011) 017201.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5526958f-b8be-4f08-b969-8332adc3eb8a
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