access icon free Personal verification based on multi-spectral finger texture lighting images

Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re-enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi-spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.

Inspec keywords: feature extraction; image classification; probability; image texture; neural nets; image sensors

Other keywords: effective classifier; blue light; wavelength 460 nm; recognition performance; cost reduction; standard PNN; white light; re-enforced probabilistic neural network; feature extraction; surrounded patterns code; multispectral illuminations; single texture descriptor; recognition model; RPNN; personal verification; finger texture images; equal error rates; multispectral FT lighting images; multispectral CASIA database; spectral lighting sensors

Subjects: Neural computing techniques; Image recognition; Image sensors; Computer vision and image processing techniques; Other topics in statistics; Other topics in statistics

References

    1. 1)
      • 20. Al-Nima, R.R.O., Dlay, S.S., Woo, W.L., et al: ‘Human authentication with finger textures based on image feature enhancement’. The Second IET Int. Conf. Intelligent Signal Processing (ISP), London, UK, 2015.
    2. 2)
      • 16. Kanhangad, V., Kumar, A., Zhang, D.: ‘A unified framework for contactless hand verification’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (3), pp. 10141027.
    3. 3)
      • 28. Khan, Z., Shafait, F., Hu, Y., et al: ‘Multispectral palmprint encoding and recognition’, 2014, arXiv preprint arXiv:1402.2941.
    4. 4)
      • 5. Al-Kaltakchi, M.T.S., Woo, W.L., Dlay, S.S., et al: ‘Study of statistical robust closed set speaker identification with feature and score-based fusion’. IEEE Statistical Signal Process Workshop (SSP), Palma de Mallorca, Spain, 2016, pp. 15.
    5. 5)
      • 7. Al-Nima, R.R.O., Dlay, S.S., Woo, W.L.: ‘A new approach to predicting physical biometrics from behavioural biometrics’, World Acad. Sci., Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng., 2014, 8, (11), pp. 19962001.
    6. 6)
      • 2. Tan, C., Kumar, A.: ‘Accurate Iris recognition at a distance using stabilized iris encoding and Zernike moments phase features’, IEEE Trans. Image Process., 2014, 23, (9), pp. 39623974.
    7. 7)
      • 8. Bhaskar, B., Veluchamy, S.: ‘Hand based multibiometric authentication using local feature extraction’. Int. Conf. Recent Trends Inf. Technol. (ICRTIT), Chennai, India, 2014, pp. 15.
    8. 8)
      • 30. Tong, Y., Chen, R., Cheng, Y.: ‘Facial expression recognition algorithm using LGC based on horizontal and diagonal prior principle’, Opt., Int. J. Light Electron Opt., 2014, 125, (16), pp. 41864189.
    9. 9)
      • 1. Darwish, S.: ‘New system to fingerprint extensible markup language documents using winnowing theory’, IET Signal Process., 2012, 6, (4), pp. 348357.
    10. 10)
      • 15. Michael, G.K.O., Connie, T., Jin, A.T.B.: ‘An innovative contactless palm print and knuckle print recognition system’, Pattern Recognit. Lett., 2010, 31, (12), pp. 17081719.
    11. 11)
      • 26. Kou, J., Xiong, S., Wan, S., et al: ‘The incremental probabilistic neural network’. Sixth Int. Conf. Natural Computation (ICNC), Yantai, China, 2010, vol. 3, pp. 13301333.
    12. 12)
      • 19. Sankaran, A., Malhotra, A., Mittal, A., et al: ‘On smartphone camera based fingerphoto authentication’. IEEE Seventh Int. Conf. Biometrics Theory, Applications, and Systems (BTAS), Arlington, VA, USA, 2015, pp. 17.
    13. 13)
      • 3. Zhang, Y., Peng, H.: ‘One sample per person face recognition via sparse representation’, IET Signal Process., 2016, 10, (9), pp. 11261134.
    14. 14)
      • 18. Zhang, Y., Sun, D., Qiu, Z.: ‘Hand-based single sample biometrics recognition’, Neural Comput. Appl., 2012, 21, (8), pp. 18351844.
    15. 15)
      • 31. Wolf, L., Hassner, T., Taigman, Y.: ‘Descriptor based methods in the wild’. Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition, Marseille, France, 2008.
    16. 16)
      • 25. Junli, L., Gengyun, Y., Guanghui, Z.: ‘Evaluation of tobacco mixing uniformity based on chemical composition’. 31st Chinese Control Conf. (CCC), Hefei, China, 2012, pp. 75527555.
    17. 17)
      • 29. Fu, X., Wei, W.: ‘Centralized binary patterns embedded with image Euclidean distance for facial expression recognition’. Fourth Int. Conf. Natural Computation, Jinan, China, 2008, vol. 4, pp. 115119.
    18. 18)
      • 24. Raghavendra, R., Busch, C.: ‘Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition’, Pattern Recognit., 2014, 47, (6), pp. 22052221.
    19. 19)
      • 32. Tao, Q., Veldhuis, R.: ‘Illumination normalization based on simplified local binary patterns for a face verification system’. Biometrics Symp., Baltimore, MD, USA, 2007, pp. 16.
    20. 20)
      • 22. Al-Nima, R.R.O., Abdullah, M.A.M., Al-Kaltakchi, M.T.S., et al: ‘Finger texture biometric verification exploiting multi-scale sobel angles local binary pattern features and score-based fusion’, Digit. Signal Process., 2017, 70, pp. 178189.
    21. 21)
      • 17. ‘The Hong Kong Polytechnic University Contact-free 3D/2D Hand Images Database version 1.0’. Available at http://www.comp.polyu.edu.hk/csajaykr/myhome/database_request/3dhand/Hand3D.htm, online database.
    22. 22)
      • 21. Al-Nima, R.R.O., Dlay, S.S., Woo, W.L., et al: ‘Efficient finger segmentation robust to hand alignment in imaging with application to human verification’. Fifmtth IEEE Int. Workshop on Biometrics and Forensics (IWBF), Coventry, UK, 2017, pp. 16.
    23. 23)
      • 23. Al-Sumaidaee, S.A.M., Abdullah, M.A.M., Al-Nima, R.R.O., et al: ‘Multi-gradient features and elongated quinary pattern encoding for image-based facial expression recognition’, Pattern Recognit., 2017, 71, pp. 249263.
    24. 24)
      • 9. Labati, R.D., Genovese, A., Ballester, E.M., et al: ‘Automatic classification of acquisition problems affecting fingerprint images in automated border controls’. IEEE Symp. Series on Computational Intelligence, Cape Town, South Africa, 2015, pp. 354361.
    25. 25)
      • 11. ‘CASIA-MS-PalmprintV1’. Available at http://biometrics.idealtest.org/, online database.
    26. 26)
      • 33. ‘IIT Delhi Palmprint Image Database version 1.0’. Available at http://www4.comp.polyu.edu.hk/csajaykr/IITD/Database_Palm.htm, online database.
    27. 27)
      • 35. Abdullah, M.A.M., Al-Nima, R.R.O., Dlay, S.S., et al: ‘Cross-spectral iris matching for surveillance applications’, in Karampelas, P., Bourlai, T. (Eds.): ‘Surveillance in action’ (Adv. Sciences and Technologies for Security Appl., Springer, Cham, 2018), pp. 105125.
    28. 28)
      • 34. Kumar, A.: ‘Incorporating cohort information for reliable palmprint authentication’. Sixth IEEE Indian Conf. Computer Vision, Graphics & Image Processing (ICVGIP'08), Bhubaneswar, India, 2008, pp. 583590.
    29. 29)
      • 4. Štruc, V., Pavešić, N.: ‘Phase congruency features for palm-print verification’, IET Signal Process., 2009, 3, (4), pp. 258268.
    30. 30)
      • 12. Ribaric, S., Fratric, I.: ‘A biometric identification system based on eigenpalm and eigenfinger features’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (11), pp. 16981709.
    31. 31)
      • 10. Al-Nima, R.R.O., Dlay, S.S., Al-Sumaidaee, S.A.M., et al: ‘Robust feature extraction and salvage schemes for finger texture based biometrics’, IET Biometrics, 2017, 6, (2), pp. 4352.
    32. 32)
      • 6. Al-Nima, R.R.O., Dlay, S.S., Woo, W.L., et al: ‘A novel biometric approach to generate ROC curve from the probabilistic neural network’. 24th IEEE Signal Processing and Communication Applications Conf. (SIU), Zonguldak, Turkey, 2016, pp. 141144.
    33. 33)
      • 13. Pavesic, N., Ribaric, S., Grad, B.: ‘Finger-based personal authentication: a comparison of feature-extraction methods based on principal component analysis, most discriminant features and regularised-direct linear discriminant analysis’, IET Signal Process., 2009, 3, (4), pp. 269281.
    34. 34)
      • 14. Michael, G.K.O., Connie, T., Jin, A.T.B.: ‘Robust palm print and knuckle print recognition system using a contactless approach’. Fifth IEEE Conf. Industrial Electronics and Applications (ICIEA), Taichung, Taiwan, 2010, pp. 323329.
    35. 35)
      • 27. Khan, Z., Mian, A., Hu, Y.: ‘Contour code: robust and efficient multispectral palmprint encoding for human recognition’. IEEE Int. Conf. Computer Vision (ICCV), Barcelona, Spain, 2011, pp. 19351942.
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