Acta Informatica Pragensia 2023, 12(1), 32-53 | DOI: 10.18267/j.aip.1902798

Classification of Eye Images by Personal Details With Transfer Learning Algorithms

Cemal Aktürk ORCID...1, Emrah Aydemir ORCID...2, Yasr Mahdi Hama Rashid3
1 Engineering and Natural Sciences Faculty, Gaziantep Islam Science and Technology University, Gaziantep, Turkey
2 Faculty of Business Administration, Sakarya University, Sakarya, Turkey
3 Department of Advanced Technologies, Kirsehir Ahi Evran University, Kirsehir, Turkey

Machine learning methods are used for purposes such as learning and estimating a feature or parameter sought from a dataset by training the dataset to solve a particular problem. The transfer learning approach, aimed at transferring the ability of people to continue learning from their past knowledge and experiences to computer systems, is the transfer of the learning obtained in the solution of a particular problem so that it can be used in solving a new problem. Transferring the learning obtained in transfer learning provides some advantages over traditional machine learning methods, and these advantages are effective in the preference of transfer learning. In this study, a total of 1980 eye contour images of 96 different people were collected in order to solve the problem of recognizing people from their eye images. These collected data were classified in terms of person, age and gender. In the classification made for eye recognition, feature extraction was performed with 32 different transfer learning algorithms in the Python program and classified using the RandomForest algorithm for person estimation. According to the results of the research, 30 different classification algorithms were used, with the ResNet50 algorithm being the most successful, and the data were also classified in terms of age and gender. Thus, the highest success rates of 83.52%, 96.41% and 77.56% were obtained in person, age and gender classification, respectively. The study shows that people can be identified only by eye images obtained from a smartphone without using any special equipment, and even the characteristics of people such as age and gender can be determined. In addition, it has been concluded that eye images can be used in a more efficient and practical biometric recognition system than iris recognition.

Keywords: Eye image; Eye recognition; Transfer learning; Classification.

Received: June 5, 2022; Revised: August 11, 2022; Accepted: August 16, 2022; Prepublished online: August 19, 2022; Published: April 19, 2023  Show citation

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Aktürk, C., Aydemir, E., & Rashid, Y.M.H. (2023). Classification of Eye Images by Personal Details With Transfer Learning Algorithms. Acta Informatica Pragensia, 12(1), 32-53. doi: 10.18267/j.aip.190
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    References

    1. Akgözlüoğlu, K. (2021). Transfer Öğrenmesi Tekniği Tabanli Derin Öğrenme Yöntemiyle Ürün Tanima. Master thesis. Manisa Celal Bayar Üniversitesi, Manisa.
    2. Azimi, M., Rasoulinejad, S. A., & Pacut, A. (2019). The effects of gender factor and diabetes mellitus on the iris recognition system's accuracy and reliability. In 2019 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA). IEEE. https://doi.org/10.23919/SPA.2019.8936757 Go to original source...
    3. Bakshi, K. A., Prasad, B., & Sneha, K. (2015). An efficient iris code storing and searching technique for Iris Recognition using non-homogeneous K-d tree. In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT). IEEE. https://doi.org/10.1109/ERECT.2015.7498983 Go to original source...
    4. Balga, M. K. (2020). Tatil Evlerindeki Odalarin Derin Öğrenme İle Siniflandirilmasi ve Uygulamasi. Master thesis. Selçuk Üniveritesi, Konya.
    5. Bayraktar, H. K. (2018). Göz İri̇s Görüntüleri̇Nde Kaoti̇k Yapinin Anali̇zi̇. Master thesis. Yalova Üniversitesi, Yalova.
    6. Bayram, F. (2020). Derin öğrenme tabanli otomatik plaka tanima. Politeknik Dergisi, 23(4), 955-960. Go to original source...
    7. Bircan, A. (2021). K-tda Sözlük öğRenmesi̇ İle Görüntü Zengi̇nleşti̇rerek İri̇s Tanima. Master thesis. Konya Teknik Üniversitesi, Konya.
    8. Cerme, G. N., & Karakaya, M. (2015). 3D iris structure impact on iris recognition. In 2015 23nd Signal Processing and Communications Applications Conference (SIU). IEEE. https://doi.org/10.1109/SIU.2015.7129977 Go to original source...
    9. ClevelandClinic. (2022). Eye Anatomy. Retrieved from https://my.clevelandclinic.org/health/body/21823-eyes
    10. Çanak, B. (2017). Alinda ve Göz Kenarlarinda Yer Alan KirişIkliklarin Siniflandirilmasi. Master thesis. İstanbul Teknik Üniversitesi, İstanbul.
    11. Çelik, S. (2020). Özellik Optimizasyonu Yapilan Beyin Manyetik Rezonans Görüntülerindeki Tümörlü Dilimlerin Transfer Öğrenmesi İle Tespiti. Master thesis. Kütahya Dumlupinar Üniversitesi, Kütahya.
    12. Danlami, M., Jamel, S., Ramli, S. N., & Azahari, S. R. M. (2020). Comparing the legendre wavelet filter and the Gabor wavelet filter for feature extraction based on Iris recognition system. In 2020 IEEE 6th International Conference on Optimization and Applications (ICOA). IEEE. https://doi.org/10.1109/ICOA49421.2020.9094465 Go to original source...
    13. Daugman, J. (2007). New Methods in Iris Recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 37(5), 1167-1175. https://doi.org/10.1109/TSMCB.2007.903540 Go to original source...
    14. Devi, C. N. (2017). Automatic segmentation and recognition of iris images: With special reference to twins. In 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN). IEEE. https://doi.org/10.1109/ICSCN.2017.8085415 Go to original source...
    15. Dillak, R. Y., & Bintiri, M. G. (2016). A novel approach for iris recognition. In 2016 IEEE Region 10 Symposium (TENSYMP). IEEE. https://doi.org/10.1109/TENCONSpring.2016.7519410 Go to original source...
    16. Doğan, F., & Türkoğlu, İ. (2019). Derin öğrenme modelleri ve uygulama alanlarina ilişkin bir derleme. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 10(2), 409-445. Go to original source...
    17. Dong, W., Sun, Z., Tan, T., & Wei, Z. (2009). Quality-based dynamic threshold for iris matching. In 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE. https://doi.org/10.1109/ICIP.2009.5413452 Go to original source...
    18. Ergün, E., & Kiliç, K. (2021). Derin Öğrenme ile Artirilmiş Görüntü Seti üzerinden Cilt Kanseri Tespiti. Black Sea Journal of Engineering and Science, 4(4), 192-200. https://doi.org/10.34248/bsengineering.938520 Go to original source...
    19. Franz-Odendaal, T. A., & Vickaryous, M. K. (2006). Skeletal elements in the vertebrate eye and adnexa: Morphological and developmental perspectives. Developmental Dynamics, 235(5), 1244-1255. https://doi.org/10.1002/dvdy.20718 Go to original source...
    20. Frigerio, E., Marcon, M., Sarti, A., & Tubaro, S. (2012). Correction method for nonideal iris recognition. In 2012 19th IEEE International Conference on Image Processing. IEEE. https://doi.org/10.1109/ICIP.2012.6467068 Go to original source...
    21. Gale, A. G., & Salankar, S. S. (2016). Evolution of performance analysis of iris recognition system by using hybrid methods of feature extraction and matching by hybrid classifier for iris recognition system. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE. https://doi.org/10.1109/ICEEOT.2016.7755308 Go to original source...
    22. Gangwar, A., & Joshi, A. (2016). DeepIrisNet: Deep iris representation with applications in iris recognition and cross-sensor iris recognition. In 2016 IEEE international conference on image processing (ICIP). IEEE. https://doi.org/10.1109/ICIP.2016.7532769 Go to original source...
    23. Gökalp, S., & Aydin, İ. (2021). Farkli Derin Sinir Aği Modellerinin Duygu Tanimadaki Performanslarinin Karşilaştirilmasi. Muş Alparslan Üniversitesi Mühendislik Mimarlik Fakültesi Dergisi, 2(1), 35-43.
    24. Hadid, A., Evans, N., Marcel, S., & Fierrez, J. (2015). Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Processing Magazine, 32(5), 20-30. https://doi.org/10.1109/MSP.2015.2437652 Go to original source...
    25. Hidimoğlu, K. (2010). Web kamera kullanimi ile parmak izi tanima ve kimlik tespiti doğrulama. http://dspace.yildiz.edu.tr/xmlui/handle/1/7905
    26. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. https://doi.org/10.48550/arXiv.1704.04861 Go to original source...
    27. Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE. https://doi.org/10.1109/CVPR.2017.243 Go to original source...
    28. Jaimes, A., Pelz, J. B., Grabowski, T., Babcock, J. S., & Chang, S.-F. (2001). Using human observer eye movements in automatic image classifiers. In Proceedings Volume 4299, Human Vision and Electronic Imaging VI. SPIE. https://doi.org/10.1117/12.429507 Go to original source...
    29. Jain, A. K., Flynn, P., & Ross, A. A. (2007). Handbook of biometrics. Springer Science & Business Media. Go to original source...
    30. Jalilian, E., Karakaya, M., & Uhl, A. (2020). End-to-end off-angle iris recognition using cnn based iris segmentation. In 2020 International Conference of the Biometrics Special Interest Group (BIOSIG). IEEE. https://ieeexplore.ieee.org/document/9210983
    31. Ng, T. W., Tay, T. L., & Khor, S. W. (2010). Iris recognition using rapid Haar wavelet decomposition. In 2010 2nd International Conference on Signal Processing Systems. IEEE. https://doi.org/10.1109/ICSPS.2010.5555246 Go to original source...
    32. Öz, M. (2021). Deri̇n Si̇ni̇r ağlari Kullanilarak Göz Bölütlemesi̇. Master thesis. Akdeniz Üniversitesi, Antalya.
    33. Özkan, İ., & Ülker, E. (2017). Derin öğrenme ve görüntü analizinde kullanilan derin öğrenme modelleri. Gaziosmanpaşa Bilimsel Araştirma Dergisi, 6(3), 85-104.
    34. Popplewell, K., Roy, K., Ahmad, F., & Shelton, J. (2014). Multispectral iris recognition utilizing hough transform and modified LBP. In 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE. https://doi.org/10.1109/SMC.2014.6974110 Go to original source...
    35. Sharkas, M. (2016). A neural network based approach for iris recognition based on both eyes. In 2016 SAI Computing Conference (SAI). IEEE. https://doi.org/10.1109/SAI.2016.7555991 Go to original source...
    36. Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556 Go to original source...
    37. Sun, Z., Zhang, H., Tan, T., & Wang, J. (2014). Iris Image Classification Based on Hierarchical Visual Codebook. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6), 1120-1133. https://doi.org/10.1109/tpami.2013.234 Go to original source...
    38. Şan, S. (2013). Parmak damar tanima teknolojisi [Finger vein identification technology]. https://openaccess.firat.edu.tr/xmlui/handle/11508/17430
    39. Tan, C.-W., & Kumar, A. (2014). Integrating ocular and iris descriptors for fake iris image detection. In 2nd International Workshop on Biometrics and Forensics. IEEE. https://doi.org/10.1109/IWBF.2014.6914251 Go to original source...
    40. Talan, T. (2021). Artificial Intelligence in Education: A Bibliometric Study. International Journal of Research in Education and Science, 7(3), 822-837. https://doi.org/10.46328/ijres.2409 Go to original source...
    41. Uçar, M. (2021). Glokom Hastaliğinin Evrişimli Sinir Aği Mimarileri ile Tespiti. Deu Muhendislik Fakultesi Fen ve Muhendislik, 23(68), 521-529. https://doi.org/10.21205/deufmd.2021236815 Go to original source...
    42. Vatsa, M., Singh, R., Ross, A., & Noore, A. (2010). Quality-based fusion for multichannel iris recognition. In 2010 20th International Conference on Pattern Recognition. IEEE. https://doi.org/10.1109/ICPR.2010.327 Go to original source...
    43. Wang, C., Muhammad, J., Wang, Y., He, Z., & Sun, Z. (2020). Towards Complete and Accurate Iris Segmentation Using Deep Multi-Task Attention Network for Non-Cooperative Iris Recognition. IEEE Transactions on Information Forensics and Security, 15, 2944-2959. https://doi.org/10.1109/tifs.2020.2980791 Go to original source...
    44. Wu, S., Zhong, S., & Liu, Y. (2018). Deep residual learning for image steganalysis. Multimedia Tools and Applications, 77(9), 10437-10453. https://doi.org/10.1007/s11042-017-4440-4 Go to original source...
    45. Zhang, M., Sun, Z., & Tan, T. (2011). Deformable DAISY matcher for robust iris recognition. In 2011 18th IEEE international conference on image processing. IEEE. https://doi.org/10.1109/ICIP.2011.6116346 Go to original source...
    46. Zhang, X., Sun, Z., & Tan, T. (2010). Hierarchical fusion of face and iris for personal identification. In 2010 20th International Conference on Pattern Recognition. IEEE. https://doi.org/10.1109/ICPR.2010.62 Go to original source...

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