http://iet.metastore.ingenta.com
1887

access icon free Chaos game theory and its application for offline signature identification

Loading full text...

Full text loading...

/deliver/fulltext/iet-bmt/8/5/IET-BMT.2018.5188.html;jsessionid=22dmm5upmna3n.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-bmt.2018.5188&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Jain, A., Ross, A.A., Nandakumar, K.: ‘Introduction to biometrics’ (Springer, US, 2011).
    2. 2)
      • 2. Fractal world gallery, credit by Cory Ench’, http://www.enchgallery.com/, accessed November 2018.
    3. 3)
      • 3. Hadjadji, B., Chibani, Y., Nemmour, H.: ‘An efficient open system for offline handwritten signature identification based on curvelet transform and one-class principal component analysis’, Neurocomputing, 2017, 265, pp. 6677.
    4. 4)
      • 4. Boudamous, F., Nemmour, H., Serdouk, Y., et al: ‘An-open system for off-line handwritten signature identification and verification using histogram of templates and SVM’. Int. Conf. Advanced Technologies for Signal and Image Processing (ATSIP), 2017, pp. 14.
    5. 5)
      • 5. Djoudjai, M.A., Chibani, Y., Abbas, N.: ‘Offline signature identification using the histogram of symbolic representation’. 5th Int. Conf. Electrical Engineering – Boumerdes (ICEE-B), 2017, pp. 16.
    6. 6)
      • 6. Hafemann, L.G., Sabourin, R., Oliveira, L.S.: ‘Learning features for offine handwritten signature verification using deep convolutional neural networks’, Pattern Recognit., 2017, 70, pp. 163176.
    7. 7)
      • 7. Morales, A., Morocho, D., Fierrez, J., et al: ‘Signature authentication based on human intervention: performance and complementarity with automatic systems’, IET Biom., 2017, 6, (4), pp. 307315.
    8. 8)
      • 8. Kumar, M.M., Puhan, N.B.: ‘Off-line signature verification: upper and lower envelope shape analysis using chord moments’, IET Biom., 2014, 3, (4), pp. 347354.
    9. 9)
      • 9. Yahyatabar, M.E., Ghasemi, J.: ‘Online signature verification using double-stage feature extraction modelled by dynamic feature stability experiment’, IET Biom., 2017, 6, (6), pp. 393401.
    10. 10)
      • 10. Hafs, T., Bennacer, L., Boughazi, M., et al: ‘Empirical mode decomposition for online handwritten signature verification’, IET Biom., 2016, 5, (3), pp. 190199.
    11. 11)
      • 11. Tang, L., Kang, W., Fang, Y.: ‘Information divergence-based matching strategy for online signature verification’, IEEE Trans. Inf. Forensics Sec., 2018, 13, (4), pp. 861873.
    12. 12)
      • 12. Ferrer, M.A., Diaz, M., Carmona-Duarte, C., et al: ‘A behavioral handwriting model for static and dynamic signature synthesis’, IEEE Trans. Pattern Anal. Mach. Intell., 2017, 39, (6), pp. 10411053.
    13. 13)
      • 13. Ferrer, M.A., Diaz-Cabrera, M., Morales, A.: ‘Synthetic off-line signature image generation’. 2013 Int. Conf. Biometrics (ICB), 2013, pp. 17.
    14. 14)
      • 14. Yilmaz, M.B., Yanikoglu, B.: ‘Score level fusion of classifiers in off-line signature verification’, Inf. Fusion, 2016, 32, pp. 109119.
    15. 15)
      • 15. Ferrer, M., Alonso, J., Travieso, C.: ‘Offine geometric parameters for automatic signature verification using fixed-point arithmetic’, PAMI, 2005, 27, (6), pp. 993997.
    16. 16)
      • 16. Sharif, M., Khan, M.A., Faisal, M., et al: ‘A framework for offline signature verification system: best features selection approach’, Pattern Recognit. Lett., 2018, https://doi.org/10.1016/j.patrec.2018.01.021.
    17. 17)
      • 17. Mandelbrot, B.: ‘Fractals: form, chance, and dimension’ (W. H. Freeman and Co, San Francisco, 1977).
    18. 18)
      • 18. Rodrigues, E.O., Liatsis, P., Satoru, L., et al: ‘Fractal triangular search: a metaheuristic for image content search’, IET Image Process., 2018, 12, (8), pp. 14751484.
    19. 19)
      • 19. Ramasamy, U., Arulprakash, G.: ‘Mid-sagittal plane detection in brain magnetic resonance image based on multifractal techniques’, IET Image Process., 2016, 10, (10), pp. 751762.
    20. 20)
      • 20. Jampour, M., Ashourzadeh, M., Yaghobi, M., et al: ‘Compressing images using fractal characteristics by estimating the nearest neighbor’. Sixth Int. Conf. Information Technology: New Generations, 2009, pp. 13191322.
    21. 21)
      • 21. Chamorro-Posada, P.: ‘A simple method for estimating the fractal dimension from digital images: the compression dimension’, Chaos, Solitons Fractals, 2016, 91, pp. 562572.
    22. 22)
      • 22. Barnsley, M.F.: ‘Fractals everywhere’ (Academic Press, USA, 1993).
    23. 23)
      • 23. Barnsley, M.F.: ‘Superfractals’ (Cambridge University Press, UK, 2006).
    24. 24)
      • 24. Deng, H.-R., Wang, Y.-H.: ‘On-line signature verification based on correlation image’. Int. Conf. Machine Learning and Cybernetics, Hebei, 2009, pp. 17881792.
    25. 25)
      • 25. Jampour, M., Estilayee, M., Naserasadi, A., et al: ‘Extract and classification of iris images by fractal dimension and efficient color of iris’, Int. J. Comput. Appl., 2011, 18, (1), pp. 1114.
    26. 26)
      • 26. Kalera, M.K., Zhang, B., Srihari, S.N.: ‘Off-line signature verification and identification using distance statistics’, Int. J. Pattern Recognit. Artif. Intell., 2004, 18, pp. 13391360.
    27. 27)
      • 27. Cedar signature dataset’, http://www.cedar.buffalo.edu/NIJ/publications.html, accessed February 2019.
    28. 28)
      • 28. Yeung, D.-Y., Chang, H., Xiong, Y., et al: ‘SVC2004: first international signature verification competition’ (Biometric Authentication. ICBA, Berlin, 2004).
    29. 29)
      • 29. Soleimani, A., Fouladi, K., Araabi, B.N.: ‘UTSig: a Persian offline signature dataset’, IET Biom., 2017, 6, (1), pp. 18.
    30. 30)
      • 30. Ferrer, M.A., Diaz-Cabrera, M., Morales, A.: ‘Static signature synthesis: a neuromotor inspired approach for biometrics’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 667680.
    31. 31)
      • 31. Maergner, P., Pondenkandath, V., Alberti, M., et al: ‘Offline signature verification by combining graph edit distance and triplet networks’, Structural, Syntactic, and Statistical Pattern Recognition, Beijing, China, 2018, pp. 470480.
    32. 32)
      • 32. Soleimani, A., Araabi, B.N., Fouladi, K.: ‘Deep multitask metric learning for offline signature verification’, Pattern Recognit. Lett., 2016, 80, pp. 8490.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2018.5188
Loading

Related content

content/journals/10.1049/iet-bmt.2018.5188
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address