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Year 2022, Volume: 9 Issue: 3, 211 - 224, 30.09.2022

Aamo Iorliam Emmanuel Orgem Yahaya I. Shehu

https://doi.org/10.54287/gujsa.1077430

Abstract

References

  • Acebo, E., & Sbert, M. (2005, May 18-20). Benford's law for natural and synthetic images. In: Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging (Computational Aesthetics'05) (pp. 169-176).
  • Bartlow, N., Kalka, N., Cukic, B., & Ross, A. (2009, June 20-25). Identifying sensors from fingerprint images. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 78-84). doi:10.1109/CVPRW.2009.5204312
  • Baştürk, A., Baştürk, N. S., & Qurbanov, O. (2018). A comparative performance analysis of various classifiers for fingerprint recognition. Omer Halisdemir University Journal of Engineering Sciences, 7(2), 504-513. doi:10.28948/ngumuh.443160
  • Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4), 551-572. https://www.jstor.org/stable/984802
  • Bonettini, N., Bestagini, P., Milani, S., & Tubaro, S. (2021, January 10-15). On the use of Benford's law to detect GAN-generated images. In: 25th International Conference on Pattern Recognition (ICPR) (pp. 5495-5502).
  • Fu, D., Shi, Y. Q., & Su, W. (2007, January 28 - February 1). A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: E. J. Delp III & P. W. Wong (Eds.), Steganography, and Watermarking of Multimedia Contents IX (SPIE 6505, Forensics III, pp. 65051L). doi:10.1117/12.704723
  • FVC2000. (2000), Fingerprint Verification Competition Databases. bias.csr.unibo.it/fvc2000/databases.asp
  • Hildebrandt, M. (2020). On digitized forensics: novel acquisition and analysis techniques for latent fingerprints based on signal processing and pattern recognition. PhD Thesis, Otto-von-Guericke-University of Magdeburg.
  • Hildebrandt, M., & Dittmann, J. (2015, February 8-12). Benford's Law based detection of latent fingerprint forgeries on the example of artificial sweat printed fingerprints captured by confocal laser scanning microscopes. In: A. M. Alattar, N. D. Memon & C. D. Heitzenrater (Eds.), Media Watermarking, Security, and Forensics 2015, (SPIE 9409, Biometric, pp. 94090A). doi:10.1117/12.2077531
  • Hildebrandt, M., Sturm, J., Dittmann, J., & Vielhauer, C. (2013, September 25-26). Creation of a public corpus of contact-less acquired latent fingerprints without privacy implications. In: B. Decker, J. Dittmann, C. Kraetzer & C. Vielhauer (Eds.), Communications and Multimedia Security, 14th IFIP TC6/TC11 International Conference (CMS 2013) (pp. 204-206). doi:10.1007/978-3-642-40779-6_19
  • Hill, T. P. (1998). The first digit phenomenon: A century-old observation about an unexpected pattern in many numerical tables applies to the stock market, census statistics and accounting data. American Scientist, 86(4), 358-363. https://www.jstor.org/stable/27857060
  • Iorliam, A. (2016). Application of power laws to biometrics, forensics and network traffic analysis. PhD Thesis, University of Surrey (United Kingdom).
  • Iorliam, A., & Shangbum, C. F. (2017). On the use of benford’s law to detect jpeg biometric data tampering. Journal of Information Security, 8(3), 240-256. doi:10.4236/jis.2017.83016
  • Iorliam, A., Ho, A. T. S., Waller, A., & Zhao, X. (2016, September 17-19). Using Benford's law divergence and neural networks for classification and source identification of biometric images. In: Y. Q. Shi, H. J. Kim, F. Perez-Gonzalez & F. Liu (Eds.), Digital Forensics and Watermarking, 15th International Workshop (IWDW 2016) (pp. 88-105). doi:10.1007/978-3-319-53465-7_7
  • Iorliam, A., Ho, A. T. S., Poh, N., Zhao, X., & Xia, Z. (2017). Benford’s law for classification of biometric images. In: C. Vielhauer (Eds.), User-Centric Privacy and Security in Biometrics (pp. 237-256). IET Digital Library. doi:10.1049/PBSE004E_ch11
  • Jain, A. K., Hong, L., Pankanti, S., & Bolle, R. (1997). An identity-authentication system using fingerprints. Proceedings of the IEEE, 85(9), 1365-1388. doi:10.1109/5.628674
  • Li, X. H., Zhao, Y. Q., Liao, M., Shih, F. Y., & Shi, Y. Q. (2012). Detection of the tampered region for JPEG images by using mode-based first digit features. EURASIP Journal on Advances in Signal Processing, 2012, 190. doi:10.1186/1687-6180-2012-190
  • Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer Science & Business Media.
  • Mishra, A., & Maheshwary, P. (2017). A novel technique for fingerprint classification based on naive bayes classifier and support vector machine. International Journal of Computer Applications, 169(7), 58-62. doi:10.5120/ijca2017914806
  • Satapathy, G., Bhattacharya, G., Puhan, N. B., & Ho, A. T. S. (2020, October 7-9). Generalized Benford’s Law for Fake Fingerprint Detection. In: D. Dey, S. Dalai, S. Ray & B. Chatterjee (Eds.), 2020 IEEE Applied Signal Processing Conference (ASPCON) (pp. 242-246). doi:10.1109/ASPCON49795.2020.9276660
  • Qi, Y., Qiu, M., Lin, H., Chen, J., Li, Y., & Lei, H. (2022). Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification. [Preprint] doi:10.21203/rs.3.rs-1399918/v1

An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images

Year 2022, Volume: 9 Issue: 3, 211 - 224, 30.09.2022

Aamo Iorliam Emmanuel Orgem Yahaya I. Shehu

https://doi.org/10.54287/gujsa.1077430

Abstract

Protecting a biometric fingerprint database against attackers is very vital in order to protect against false acceptance rate or false rejection rate. A key property in distinguishing biometric fingerprint images is by exploiting the characteristics of these different types of fingerprint images. The aim of this paper is to perform an intra-class classification of fingerprint images using Benford's law divergence values and machine learning techniques. The usage of these Benford’s law divergence values as features fed into the machine learning techniques has proved to be very effective and efficient in the intra-class classification of biometric fingerprint images. The effectiveness of our proposed methodology was demonstrated on five datasets resulting in a total of 367 samples. All the machine learning techniques used in this experiment were trained using the k-fold cross validation and the dataset was split into ten times (10-folds). The models achieved high intra-class classification mean accuracies of 99.72% for the Convolutional Neural Networks (CNN), and 95.90% for the Naïve Bayes. Again, the Decision Tree and Logistic Regression, achieved accuracies of 95.62%, and 94.47%, respectively. These results showed that Benford’s law features and machine learning techniques, especially the CNN and Naïve Bayes can be effectively applied for the intra-class classification of fingerprint images. The implication of these results is that the different types of fingerprint images can be effectively discriminated using Benford's law divergence values and machine learning technique for forensics and biometrics applications.

Keywords

Benford’s Law Divergence Values Machine Learning Techniques Intra-Class Separability Fingerprint Images

References

  • Acebo, E., & Sbert, M. (2005, May 18-20). Benford's law for natural and synthetic images. In: Proceedings of the First Eurographics Conference on Computational Aesthetics in Graphics, Visualization and Imaging (Computational Aesthetics'05) (pp. 169-176).
  • Bartlow, N., Kalka, N., Cukic, B., & Ross, A. (2009, June 20-25). Identifying sensors from fingerprint images. In: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (pp. 78-84). doi:10.1109/CVPRW.2009.5204312
  • Baştürk, A., Baştürk, N. S., & Qurbanov, O. (2018). A comparative performance analysis of various classifiers for fingerprint recognition. Omer Halisdemir University Journal of Engineering Sciences, 7(2), 504-513. doi:10.28948/ngumuh.443160
  • Benford, F. (1938). The law of anomalous numbers. Proceedings of the American Philosophical Society, 78(4), 551-572. https://www.jstor.org/stable/984802
  • Bonettini, N., Bestagini, P., Milani, S., & Tubaro, S. (2021, January 10-15). On the use of Benford's law to detect GAN-generated images. In: 25th International Conference on Pattern Recognition (ICPR) (pp. 5495-5502).
  • Fu, D., Shi, Y. Q., & Su, W. (2007, January 28 - February 1). A generalized Benford’s law for JPEG coefficients and its applications in image forensics. In: E. J. Delp III & P. W. Wong (Eds.), Steganography, and Watermarking of Multimedia Contents IX (SPIE 6505, Forensics III, pp. 65051L). doi:10.1117/12.704723
  • FVC2000. (2000), Fingerprint Verification Competition Databases. bias.csr.unibo.it/fvc2000/databases.asp
  • Hildebrandt, M. (2020). On digitized forensics: novel acquisition and analysis techniques for latent fingerprints based on signal processing and pattern recognition. PhD Thesis, Otto-von-Guericke-University of Magdeburg.
  • Hildebrandt, M., & Dittmann, J. (2015, February 8-12). Benford's Law based detection of latent fingerprint forgeries on the example of artificial sweat printed fingerprints captured by confocal laser scanning microscopes. In: A. M. Alattar, N. D. Memon & C. D. Heitzenrater (Eds.), Media Watermarking, Security, and Forensics 2015, (SPIE 9409, Biometric, pp. 94090A). doi:10.1117/12.2077531
  • Hildebrandt, M., Sturm, J., Dittmann, J., & Vielhauer, C. (2013, September 25-26). Creation of a public corpus of contact-less acquired latent fingerprints without privacy implications. In: B. Decker, J. Dittmann, C. Kraetzer & C. Vielhauer (Eds.), Communications and Multimedia Security, 14th IFIP TC6/TC11 International Conference (CMS 2013) (pp. 204-206). doi:10.1007/978-3-642-40779-6_19
  • Hill, T. P. (1998). The first digit phenomenon: A century-old observation about an unexpected pattern in many numerical tables applies to the stock market, census statistics and accounting data. American Scientist, 86(4), 358-363. https://www.jstor.org/stable/27857060
  • Iorliam, A. (2016). Application of power laws to biometrics, forensics and network traffic analysis. PhD Thesis, University of Surrey (United Kingdom).
  • Iorliam, A., & Shangbum, C. F. (2017). On the use of benford’s law to detect jpeg biometric data tampering. Journal of Information Security, 8(3), 240-256. doi:10.4236/jis.2017.83016
  • Iorliam, A., Ho, A. T. S., Waller, A., & Zhao, X. (2016, September 17-19). Using Benford's law divergence and neural networks for classification and source identification of biometric images. In: Y. Q. Shi, H. J. Kim, F. Perez-Gonzalez & F. Liu (Eds.), Digital Forensics and Watermarking, 15th International Workshop (IWDW 2016) (pp. 88-105). doi:10.1007/978-3-319-53465-7_7
  • Iorliam, A., Ho, A. T. S., Poh, N., Zhao, X., & Xia, Z. (2017). Benford’s law for classification of biometric images. In: C. Vielhauer (Eds.), User-Centric Privacy and Security in Biometrics (pp. 237-256). IET Digital Library. doi:10.1049/PBSE004E_ch11
  • Jain, A. K., Hong, L., Pankanti, S., & Bolle, R. (1997). An identity-authentication system using fingerprints. Proceedings of the IEEE, 85(9), 1365-1388. doi:10.1109/5.628674
  • Li, X. H., Zhao, Y. Q., Liao, M., Shih, F. Y., & Shi, Y. Q. (2012). Detection of the tampered region for JPEG images by using mode-based first digit features. EURASIP Journal on Advances in Signal Processing, 2012, 190. doi:10.1186/1687-6180-2012-190
  • Maltoni, D., Maio, D., Jain, A. K., & Prabhakar, S. (2009). Handbook of fingerprint recognition. Springer Science & Business Media.
  • Mishra, A., & Maheshwary, P. (2017). A novel technique for fingerprint classification based on naive bayes classifier and support vector machine. International Journal of Computer Applications, 169(7), 58-62. doi:10.5120/ijca2017914806
  • Satapathy, G., Bhattacharya, G., Puhan, N. B., & Ho, A. T. S. (2020, October 7-9). Generalized Benford’s Law for Fake Fingerprint Detection. In: D. Dey, S. Dalai, S. Ray & B. Chatterjee (Eds.), 2020 IEEE Applied Signal Processing Conference (ASPCON) (pp. 242-246). doi:10.1109/ASPCON49795.2020.9276660
  • Qi, Y., Qiu, M., Lin, H., Chen, J., Li, Y., & Lei, H. (2022). Research on Gender-related Fingerprint Features, Extracting Fingerprint Features Using Autoencoder Networks for Gender Classification. [Preprint] doi:10.21203/rs.3.rs-1399918/v1
There are 21 citations in total.

Details

Primary Language English
Journal Section Computer Science
Authors

Aamo Iorliam 0000-0001-8238-9686

Emmanuel Orgem This is me 0000-0003-3211-876X

Yahaya I. Shehu This is me 0000-0001-8924-9344

Publication Date September 30, 2022
Submission Date February 22, 2022
Published in Issue Year 2022 Volume: 9 Issue: 3

Cite

APA Iorliam, A., Orgem, E., & Shehu, Y. I. (2022). An Investigation of Benford’s Law Divergence and Machine Learning Techniques for Intra-Class Separability of Fingerprint Images. Gazi University Journal of Science Part A: Engineering and Innovation, 9(3), 211-224. https://doi.org/10.54287/gujsa.1077430
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