Research Article
Year 2021,
Volume: 24 Issue: 3, 983 - 988, 01.09.2021
Abstract
The image of biometric properties in humans is used in many fields today. Regardless of these features, it is necessary to first translate it into data that the computer understands. In this study, automatic and dynamic image segmentation was performed by using 300x300 fingerprint images. A fingerprint database with a total of 80 images and 10 different classes was used. The features of the images were subtracted from the sub-segments obtained from these images by the feature extraction algorithm that was originally developed. The 300x300 images were divided into 25x25 sub-images and the feature vector was obtained. 144x80 inputs obtained after image segmentation were kept in areas in separate tables. The developed segmentation and feature extraction algorithm can be applied to any image of equal size.
References
- Jain A., Hong, L., & Pankanti, S., “Biometric identification”, Communications of the ACM, 43(2): 90-98, (2000).
- Sahasrabudhe M., “Fingerprint Image Enhancement Using Unsupervised Hierarchical Feature Learning”, Doctoral dissertation. Hyderabad: International Institute of Information Technology, (2015).
- Pankanti S., Prabhakar S., Jain A. K., “On the individuality of fingerprints”, “IEEE Transactions on pattern analysis and machine intelligence”, 24(8): 1010-1025, (2002).
- Wang R., Han C., Wu Y., Guo T., “Fingerprint Classification Based on Depth Neural Network”, preprint arXiv:1409.5188, (2014).
- Kaur M., Singh M., Girdhar A., Sandhu P. S., “Fingerprint verification system using minutiae extraction technique”, World Academy of Science, Engineering and Technology, 46: 497-502, (2008).
- Jiang L., Zhao T., Bai C., Yong A., Wu M., “A direct fingerprint minutiae extraction approach based on convolutional neural networks”, In Neural Networks (IJCNN), 2016 International Joint Conference, IEEE, 571-578, (2016).
- Ratha N. K., Karu K., Chen S., Jain A. K., “A real-time matching system for large fingerprint databases”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8): 799-813, (1996).
- Vatsa M., Singh R., Noore A., Singh S. K., “Combining pores and ridges with minutiae for improved fingerprint verification”, Signal Processing, 89(12): 2676-2685, (2009).
- Coetzee L., Botha E. C., “Fingerprint recognition in low quality images”, Pattern recognition, 26(10): 1441-1460, (1993).
- Hoi L., Duy B., “Online fingerprint identification with a fast and distortion tolerant hashing”, Journal of Information Assurance and Security, 4: 117-123, (2009).
- Jain A., Chen Y., Demirkus M., “August. Pores and ridges: Fingerprint matching using level 3 features”, In Pattern Recognition, 2006. ICPR 2006. 18th International Conference, IEEE, 4: 477-480, (2006).
- Bolle, R. M., Connell, J. H., Pankanti, S., Ratha, N. K., & Senior, A. W., Guide to biometrics. Springer Science & Business Media, (2013).
- Cui, W., Wu, G., Hua, R., & Yang, H., 2008, September. The research of edge detection algorithm for Fingerprint images. In Automation Congress, 2008. WAC World IEEE, 1-5, (2008).
- Shunshan L., Min W., Haiying T., Tiange Z., Buonocore M. H., “Image enhancement method for fingerprint recognition system”, In 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS, (2005).
- Mil'Shtein S., Pillai A., Shendye A., Liessner C., Baier M., “Fingerprint recognition algorithms for partial and full fingerprints”, In Technologies for Homeland Security, 2008 IEEE Conference, 449- 452, (2008).
- Maio D. and Maltoni D., “A structural approach to fingerprint classification”, in Proceedings of the 13th International Conference on Pattern Recognition, vol. 3. IEEE, 578–585,(1996).
- Cappelli R., Lumini A., Maio D., and Maltoni D., “Fingerprint classification by directional image partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5): 402–421, (1999).
- Senior A., “A combination fingerprint classifier”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10): 1165– 1174, (2001).
- Chang J. H. and Fan K. C., “A new model for fingerprint classification by ridge distribution sequences”, Pattern Recognition, 35(6): 1209–1223, (2002).
- Nagaty K. A., “Fingerprints classification using artificial neural networks: a combined structural and statistical approach”, Neural Networks, 14(9): 1293–1305, (2001).
- Web site, http://bias.csr.unibo.it/fvc2000/download.asp, access date:14.5.2019.
Year 2021,
Volume: 24 Issue: 3, 983 - 988, 01.09.2021
Abstract
The image of biometric properties in humans is used in many fields today. Regardless of these features, it is necessary to first translate it into data that the computer understands. In this study, automatic and dynamic image segmentation was performed by using 300x300 fingerprint images. A fingerprint database with a total of 80 images and 10 different classes was used. The features of the images were subtracted from the sub-segments obtained from these images by the feature extraction algorithm that was originally developed. The 300x300 images were divided into 25x25 sub-images and the feature vector was obtained. 144x80 inputs obtained after image segmentation were kept in areas in separate tables. The developed segmentation and feature extraction algorithm can be applied to any image of equal size.
References
- Jain A., Hong, L., & Pankanti, S., “Biometric identification”, Communications of the ACM, 43(2): 90-98, (2000).
- Sahasrabudhe M., “Fingerprint Image Enhancement Using Unsupervised Hierarchical Feature Learning”, Doctoral dissertation. Hyderabad: International Institute of Information Technology, (2015).
- Pankanti S., Prabhakar S., Jain A. K., “On the individuality of fingerprints”, “IEEE Transactions on pattern analysis and machine intelligence”, 24(8): 1010-1025, (2002).
- Wang R., Han C., Wu Y., Guo T., “Fingerprint Classification Based on Depth Neural Network”, preprint arXiv:1409.5188, (2014).
- Kaur M., Singh M., Girdhar A., Sandhu P. S., “Fingerprint verification system using minutiae extraction technique”, World Academy of Science, Engineering and Technology, 46: 497-502, (2008).
- Jiang L., Zhao T., Bai C., Yong A., Wu M., “A direct fingerprint minutiae extraction approach based on convolutional neural networks”, In Neural Networks (IJCNN), 2016 International Joint Conference, IEEE, 571-578, (2016).
- Ratha N. K., Karu K., Chen S., Jain A. K., “A real-time matching system for large fingerprint databases”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(8): 799-813, (1996).
- Vatsa M., Singh R., Noore A., Singh S. K., “Combining pores and ridges with minutiae for improved fingerprint verification”, Signal Processing, 89(12): 2676-2685, (2009).
- Coetzee L., Botha E. C., “Fingerprint recognition in low quality images”, Pattern recognition, 26(10): 1441-1460, (1993).
- Hoi L., Duy B., “Online fingerprint identification with a fast and distortion tolerant hashing”, Journal of Information Assurance and Security, 4: 117-123, (2009).
- Jain A., Chen Y., Demirkus M., “August. Pores and ridges: Fingerprint matching using level 3 features”, In Pattern Recognition, 2006. ICPR 2006. 18th International Conference, IEEE, 4: 477-480, (2006).
- Bolle, R. M., Connell, J. H., Pankanti, S., Ratha, N. K., & Senior, A. W., Guide to biometrics. Springer Science & Business Media, (2013).
- Cui, W., Wu, G., Hua, R., & Yang, H., 2008, September. The research of edge detection algorithm for Fingerprint images. In Automation Congress, 2008. WAC World IEEE, 1-5, (2008).
- Shunshan L., Min W., Haiying T., Tiange Z., Buonocore M. H., “Image enhancement method for fingerprint recognition system”, In 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS, (2005).
- Mil'Shtein S., Pillai A., Shendye A., Liessner C., Baier M., “Fingerprint recognition algorithms for partial and full fingerprints”, In Technologies for Homeland Security, 2008 IEEE Conference, 449- 452, (2008).
- Maio D. and Maltoni D., “A structural approach to fingerprint classification”, in Proceedings of the 13th International Conference on Pattern Recognition, vol. 3. IEEE, 578–585,(1996).
- Cappelli R., Lumini A., Maio D., and Maltoni D., “Fingerprint classification by directional image partitioning”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(5): 402–421, (1999).
- Senior A., “A combination fingerprint classifier”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10): 1165– 1174, (2001).
- Chang J. H. and Fan K. C., “A new model for fingerprint classification by ridge distribution sequences”, Pattern Recognition, 35(6): 1209–1223, (2002).
- Nagaty K. A., “Fingerprints classification using artificial neural networks: a combined structural and statistical approach”, Neural Networks, 14(9): 1293–1305, (2001).
- Web site, http://bias.csr.unibo.it/fvc2000/download.asp, access date:14.5.2019.
There are 21 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Article |
| Authors | |
| Publication Date | September 1, 2021 |
| Submission Date | December 27, 2019 |
| Published in Issue | Year 2021 Volume: 24 Issue: 3 |
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