(45-6) 10 * << * >> * Russian * English * Content * All Issues
Retinal biometric identification using convolutional neural network
Rodiah 1, Sarifuddin Madenda 2, Diana Tri Susetianingtias 1, Fitrianingsih 1, Dea Adlina 1, Rini Arianty 1
1 Departement of Informatics Gunadarma University,
Margonda Raya Street Number 100, Pondok Cina, Depok, West Java, 16431, Indonesia,
2 Doctoral Program in Information Tech Gunadarma University,
Margonda Raya Street Number 100, Pondok Cina, Depok, West Java, 16431, Indonesia
PDF, 1463 kB
Full text of article: English language.
Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.
blood vessels, convolutional neural network, identification, retina, segmentation.
Rodiah, Madenda S, Susetianingtias DT, Fitrianingsih, Adlina D, Arianty R. Retinal biometric identification using convolutional neural network. Computer Optics 2021; 45(6): 865-872. DOI: 10.18287/2412-6179-CO-890.
The work was partially funded by DP2M RistekDikti, Gunadarma University especially to the Gunadarma University Research Bureau for the opportunity to conduct research specifically in the field of Biometrics.
- Addy D, Bala P. Physical access control based on biometrics and GSM. Int Conf on Advances in Computing, Communications and Informatics (ICACCI) 2016: 1995-2001. DOI: 10.1109/ICACCI.2016.7732344.
- Okokpujie K, Noma-Osaghae E, Okesola O, John SN, Okonigene RE. Design and implementation of a student attendance system using Iris biometric recognition. Int Conf on Computational Science and Computational Intelligence 2017: 563-567. DOI: 10.1109/CSCI.2017.96.
- Kalyani CH. Various biometric authentiocation techniques: a review. J Biom Biostat 2017; 8(5). DOI: 10.4172/2155-6180.1000371.
- Okokpujie K, Uduehi O, Edeko F. An enhanced biometric atm with gsm feedback mechanism. J Electr Electron Eng 2015; 12: 68-81.
- Kihal N, Chitroub S, Polette A, Brunette I, Meunier J. Efficient multimodal ocular biometric system for person authentication based on iris texture and corneal shape. IET Biom 2017; 6(6): 379-386. DOI: 10.1049/iet-bmt.2016.0067.
- Okokpujie K, Olajide F, John S, Kennedy CG. Implementation of the enhanced fingerprint authentication in the ATM system using ATmega128 with GSM feedback mechanism. Int Conf on Security and Management (SAM) 2016. Source: <https://www.researchgate.net/profile/Kennedy-Okokpujie/publication/318876644_Implementation_of_the_Enhanced_Fingerprint_Authentication_in_the_ATM_System_Using_ATmega128_with_GSM_Feedback_Mechanism/links/5982d260458515a60df81382/Implementation-of-the-Enhanced-Fingerprint-Authentication-in-the-ATM-System-Using-ATmega128-with-GSM-Feedback-Mechanism.pdf>.
- Unar JA, Seng WC, Abbasi A. A review of biometric technology along with trends and prospects. Patt Recogn 2017; 47(8): 2673-2688. DOI: 10.1016/j.patcog.2014.01.016.
- Ogbanufe O, Kim DJ. Comparing fingerprint-based biometrics authentication versus traditional authentication methods for e-payment. Decis Support Syst 2017; 106: 1-14. DOI: 10.1016/j.dss.2017.11.003.
- Mudholkar SS. Biometrics authentication technique for intrusion detection systems using fingerprint recognition. International Journal of Computer Science, Engineering and Information Technology 2012; 2(1): 57-65. DOI: 10.5121/ijcseit.2012.2106.
- Wang Z, Xian J, Man F, Zhang Z. Diagnostic imaging of ophthalmology: A practical atlas. 1st ed. China Mainland: People’s Military Medical Press; 2018. ISBN: 978-94-024-1058-7.
- Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res 2014; 15: 1929-1958.
- Sadikoglu F, Uzelaltinbulat S. Biometric retina identification based on neural network. Procedia Comput Sci 2016; 102: 26-33. DOI: 10.1016/j.procs.2016.09.365.
- Khokher R, Singh RC, Jain A. Verification of biometric traits using deep learning. IJITEE 2019; 8(10): 452-459. DOI: 10.35940/ijitee.J1083.08810S19.
- Butt MM, Latif G, Iskandar DNFA, Alghazo J, Khan AH. Multi-channel convolutions neural network based diabetic retinopathy detection from fundus images. Procedia Comput Sci 2019; 163: 283-291. DOI: 10.1016/j.procs.2019.12.110.
- Yang W, Wang S, Hu J, Zheng G, Valli C. A fingerprint and finger-vein based cancelable multi-biometric system. Patt Recogn 2018; 78: 242-251. DOI 10.1016/j.patcog.2018.01.026.
- Soleymani S, Dabouei A, Kazemi H, Dawson J, Nasrabadi NM. Multi-level feature abstraction from convolutional neural networks for multimodal biometric identification. 24th Int Conf on Pattern Recognition 2018: 3469-3476. DOI: 10.1109/ICPR.2018.8545061.
- Fu H, Xu Y, Wong DWK, Liu J. Retinal vessel segmentation via deep learning network and fully-connected conditional random fields. 13th International Symposium on Biomedical Imaging 2016: 698-701. DOI: 10.1109/ISBI.2016.7493362.
- DRIVE: Digital Retinal Images for Vessel Extraction. Source: <https://drive.grand-challenge.org/>.
- Susetianingtias DT, Madenda S, Fitrianingsih, Adlina D, Rodiah, Arianty R. Retinal blood vessel extraction using wavelet decomposition. Int J Adv Comput Sci Appl 2020; 11(4): 351-355. DOI: 10.14569/IJACSA.2020.0110448.
- Sasidharan G. Retinal based personal identification system using skeletonization and similarity transformation. IJCTT 2014; 17(3): 144-147. DOI: 10.14445/22312803/IJCTT-V17P127.
- Fatima J, Syed AM, Akram MU. A secure personal identification system based on human retina. IEEE Symposium on Industrial Electronics & Applications 2013: 90-95. DOI: 10.1109/ISIEA.2013.6738974.
© 2009, IPSI RAS
151, Molodogvardeiskaya str., Samara, 443001, Russia; E-mail: firstname.lastname@example.org ; Tel: +7 (846) 242-41-24 (Executive secretary), +7 (846) 332-56-22 (Issuing editor), Fax: +7 (846) 332-56-20