Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

Chaos game theory and its application for offline signature identification

Chaos game theory and its application for offline signature identification

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Chaos game mechanism is a procedure for creating fractal phenomena from another fractal using a polygon and random walk. Here, the authors propose a novel identification approach and efficient application of Chaos Game theory towards signature identification. In fact, a signature is produced with hand scripting with a special rhythm that belongs to a specific person. Although a signature is a behavioural feature among the human biometric features, this behavioural feature expresses a chaotic property and can be analysed with chaotic systems and fractal theory. With authors’ technique, by using a Chaos Game theory, a new fractal is created for each signature instance and, during the creation of the fractal, new features are extracted. These features express the fractal properties of the signature and are unique. In addition, by using fractal theory, this technique benefits from the advantages of fractal phenomena such as stability against rotation, losing some parts of the signature and scale that is desirable for biometrics applications. Authors’ approach for offline signature analysis can segregate and identify many signature instances with a desirable time complexity. The authors name the technique that the authors present here the chaos game signature identification (CGSI).

References

    1. 1)
      • 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.
    2. 2)
      • 22. Barnsley, M.F.: ‘Fractals everywhere’ (Academic Press, USA, 1993).
    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)
      • 17. Mandelbrot, B.: ‘Fractals: form, chance, and dimension’ (W. H. Freeman and Co, San Francisco, 1977).
    5. 5)
      • 2. Fractal world gallery, credit by Cory Ench’, http://www.enchgallery.com/, accessed November 2018.
    6. 6)
      • 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.
    7. 7)
      • 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.
    8. 8)
      • 10. Hafs, T., Bennacer, L., Boughazi, M., et al: ‘Empirical mode decomposition for online handwritten signature verification’, IET Biom., 2016, 5, (3), pp. 190199.
    9. 9)
      • 28. Yeung, D.-Y., Chang, H., Xiong, Y., et al: ‘SVC2004: first international signature verification competition’ (Biometric Authentication. ICBA, Berlin, 2004).
    10. 10)
      • 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.
    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)
      • 32. Soleimani, A., Araabi, B.N., Fouladi, K.: ‘Deep multitask metric learning for offline signature verification’, Pattern Recognit. Lett., 2016, 80, pp. 8490.
    13. 13)
      • 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.
    14. 14)
      • 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.
    15. 15)
      • 29. Soleimani, A., Fouladi, K., Araabi, B.N.: ‘UTSig: a Persian offline signature dataset’, IET Biom., 2017, 6, (1), pp. 18.
    16. 16)
      • 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.
    17. 17)
      • 1. Jain, A., Ross, A.A., Nandakumar, K.: ‘Introduction to biometrics’ (Springer, US, 2011).
    18. 18)
      • 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.
    19. 19)
      • 23. Barnsley, M.F.: ‘Superfractals’ (Cambridge University Press, UK, 2006).
    20. 20)
      • 14. Yilmaz, M.B., Yanikoglu, B.: ‘Score level fusion of classifiers in off-line signature verification’, Inf. Fusion, 2016, 32, pp. 109119.
    21. 21)
      • 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.
    22. 22)
      • 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.
    23. 23)
      • 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.
    24. 24)
      • 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.
    25. 25)
      • 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.
    26. 26)
      • 13. Ferrer, M.A., Diaz-Cabrera, M., Morales, A.: ‘Synthetic off-line signature image generation’. 2013 Int. Conf. Biometrics (ICB), 2013, pp. 17.
    27. 27)
      • 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.
    28. 28)
      • 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.
    29. 29)
      • 27. Cedar signature dataset’, http://www.cedar.buffalo.edu/NIJ/publications.html, accessed February 2019.
    30. 30)
      • 15. Ferrer, M., Alonso, J., Travieso, C.: ‘Offine geometric parameters for automatic signature verification using fixed-point arithmetic’, PAMI, 2005, 27, (6), pp. 993997.
    31. 31)
      • 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.
    32. 32)
      • 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.
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