Description
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The project presents robust palm vein recognition using hybrid texture descriptors such as discriminative robust local ternary pattern and Weber?s local descriptor for improving the recognition accuracy. A Biometric system is essentially a pattern recognition system that makes use of biometric traits to recognize individuals. There was a negative effect on recognition performance on fingerprint and palmprint biometrics due to the some conditions such as oil on the fingers, moisture, and dirt. Therefore, vein patterns stand out from the host of intrinsic biometric traits for development of a recognition system that can meet all these expectations. Vein patterns are the network structure of blood vessels underneath the human skin that are almost invisible to the naked eye under natural lighting conditions and can be acquired in vivo only when employing infrared illumination, which effectively protects against possible external damage, spoof attacks, and impersonation. The texture of the blood vessels of different individuals has been proven to be distinctive even among identical twins. Initially the palm vein images are preprocessed to select the region of interest for vein pattern extraction. Here , we are using CNN for the classification purpose.
EXISTING METHOD
- Fingerprint/vein based person authentication
- Gabor filter and Discrete wavelet transform
- PCA and Local binary pattern
DRAWBACKS
- Less efficiency and not flexible in authentication scheme
- Poor discriminatory power
- Inefficient texture features due to shift variance
- Less accuracy for various lighting condition of images due to the delivery of insufficient descriptors.
PROPOSED METHOD
Robust palm vein pattern recognition system based on,
- Pre-processing
- Region of Interest
- CNN
ADVANTAGES
- Low complexity and better flexibility in vein pattern extraction
- Descriptors provides local contrast and luminance invariant features
- It provides better recognition accuracy
APPLICATIONS
- Defense weapon storage Area
- Bank ATM security
- Authentication and privacy protection
SOFTWARE REQUIREMENT
- MATLAB 2018A
REFERENCES
[1] Y. Zhou and A. Kumar, ?Human identification using palm-vein images,? IEEE Trans. Inf. Forensics Security, vol. 6, no. 4, pp. 1259?1274, Dec. 2011.
[2] L. Mirmohamadsadeghi and A. Drygajlo, ?Palm vein recognition with local binary patterns and local derivative patterns,? in Proc. Int. Joint Conf. Biometrics, Oct. 2011, pp. 1?6
[3] M. Fischer, M. Rybnicek, and S. Tjoa, ?A novel palm vein recognition approach based on enhanced local Gabor binary patterns histogram sequence,? in Proc. 19th Int. Conf. Syst., Signals, Image Process., Apr. 2012, pp. 429?432.
[4] J.-C. Lee, ?A novel biometric system based on palm vein image,? Pattern Recognit. Lett., vol. 33, no. 12, pp. 1520?1528, Sep. 2012.
[5] Y. Zhou and A. Kumar, ?Contactless palm vein identification using multiple representations,? in Proc. 4th IEEE Int. Conf. Biometrics, Theory Appl. Syst., Sep. 2010, pp. 1?6.
[6] E. C. Lee, H. C. Lee, and K. R. Park, ?Finger vein recognition using minutia-based alignment and local binary pattern-based feature extraction,? Int. J. Imag. Syst. Technol., vol. 19, no. 3, pp. 179?186, 2009.
[7] H. C. Lee, B. J. Kang, E. C. Lee, and K. R. Park, ?Finger vein recognition using weighted local binary pattern code based on a support vector machine,? J. Zhejiang Univ. Sci. C, vol. 11, no. 7, pp. 514?524, 2010.
[8] W. Kang, Y. Liu, Q. Wu, and X. Yue, ?Contact-free palm-vein recognition based on local invariant features,? PLoS One, vol. 9, no. 5, p. e97548, 2014
[9] P.-O. Ladoux, C. Rosenberger, and B. Dorizzi, ?Palm vein verification system based on SIFT matching,? in Proc. 3rd Int. Conf. Adv. Biometrics, 2009, pp. 1290?1298.
[10] M. Pan and W. Kang, ?Palm vein recognition based on three local invariant feature extraction algorithms,? in Proc. 6th Chin. Conf. Biometric Recognit., 2011, pp. 116?124.
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