Humans Verification by Adopting Deep Recurrent Fingerphotos Network
Main Article Content
Abstract
Fingerphoto can be considered as one of recent and interesting biometrics. It basically means a fingerprint image that is acquired by a smartphone in contactless manner. This paper proposes a new Deep Recurrent Learning (DRL) approach for verifying humans based on their fingerphoto image. It is called the Deep Recurrent Fingerphotos Network (DRFN). It compromises of input layer, sequence of hidden layers, output layer and essential feedback. The proposed DRFN sequentially accepts fingerphoto images of all personal fingers. It has the capability to change between the weights of each individual fingerphoto and provide verification. A huge number of fingerphoto images have been acquired, arranged, segmented and utilized as a useful dataset in this paper. It is named the Fingerphoto Images of Ten Fingers (FITF) dataset. Average accuracy result of 99.84 % is obtained for personal verification by exploiting fingerphotos.
Received 28/12/2023
Revised 19/04/2024
Accepted 21/04/2024
Published 25/05/2024
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
References
Raid R O. Signal Processing and Machine Learning Techniques for Human Verification Based on Finger Textures. 2017; p. 1–195.
Horkaew P, Khaminkure A, Suesat N, Puttinaovarat S. Eyewitnesses’ Visual Recollection in Suspect Identification by using Facial Appearance Model. Baghdad Sci.J. 2020; 17(1): 190-198 https://doi.org/10.21123/bsj.2022.6145
Radhi AM, Mohammed SA. Enhancement Ear-based Biometric System Using a Modified AdaBoost Method. Baghdad Sci J. 2022; 19(6): 1346-1346 https://doi.org/10.21123/bsj.2022.6322
Shin HS, Bergbreiter S. Effect of finger geometries on strain response of interdigitated capacitor based soft strain sensors. Appl Phys Lett. 2018 Jan 22;112(4). https://doi.org/10.1063/1.4998440
Malassiotis S, Aifanti N, Strintzis MG. Personal authentication using 3-D finger geometry. IEEE Trans Inf Forensics Secur . 2006; 1(1): 12-21. https://doi.org/10.1109/TIFS.2005.863508
Najeeb SM, Al-Nima RR, Al-Dabag ML. Reinforced Deep Learning for Verifying Finger Veins. International Int J Online Biomed Eng. 2021 Jul 1; 17(7). https://doi.org/10.3991/ijoe.v17i07.24655
Sidiropoulos GK, Kiratsa P, Chatzipetrou P, Papakostas GA. Feature extraction for finger-vein-based identity recognition. J Imaging . 2021 May 15; 7(5): 89. https://doi.org/10.3390/jimaging7050089.
Al-Nima RRO, Jarjes MK, Kasim AW, Sheet SSM. Human Identification using Local Binary Patterns for Finger Outer Knuckle. IEEE 8th Conference on Systems, Process and Control (ICSPC) 2020 Dec 11 (pp. 7-12). https://doi.org/10.1109/ICSPC50992.2020.9305779
Liu M, Tian Y, Ma Y. Inner-knuckle-print recognition based on improved LBP. Lect Notes Electr Eng. 2013; 212: 623-630 . Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-34531-9_66
Bahmed F, Mammar MO, Ouamri A. A Multimodal Hand Recognition System Based on Finger Inner-Knuckle Print and Finger Geometry. J Appl Secur Res. 2019 Jan 2; 14(1): 48-73. https://doi.org/10.1080/19361610.2019.1545271
Al-Nima RRO, Al-Kaltakchi MTS, Al-Sumaidaee SAM, Dlay SS, Woo WL, Han T, et al. Personal verification based on multi-spectral finger texture lighting images. IET Signal Proc. 2018; 12(9) :1154-64. https://doi.org/10.1049/iet-spr.2018.5091
Al-Nima RRO, Dlay SS, Al-Sumaidaee SAM, Woo WL, Chambers JA. Robust feature extraction and salvage schemes for finger texture based biometrics. IET Biom. 2017; 6(2). https://doi.org/10.1049/iet-bmt.2016.0090
Omar Al-Nima RR, Hasan SQ, Mahmood SE. Utilizing Fingerphotos with Deep Learning Techniques to Recognize Individuals. Ntu Jet. 2023; 2(1). https://doi.org/10.56286/ntujet.v2i1.318
Agarwal D, Mangal D. Comparative Analysis of Color-Based Segmentation Methods Used for Smartphone Camera Captured Fingerphotos. ICSISCET .2022. https://doi.org/10.1007/978-981-19-1653-3_5
Marasco E, Vurity A. Fingerphoto Presentation Attack Detection: Generalization in Smartphones. In: Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021. 2021. https://doi.org/10.23919/BIOSIG.2018.8553577
Hasan MM, Nasrabadi N, Dawson J. On improving interoperability for cross-domain multi-finger fingerprint matching using coupled adversarial learning. IET Biom. 2023; 12(4): 194–210. https://doi.org/10.1049/bme2.12117
Venkatesh S. Multi-spectral Finger based User Verification using Off-the-Shelf Deep Features. IST 2022 - IEEE International Conference on Imaging Systems and Techniques, Proceedings. 2022;(ii). https://doi.org/10.1109/IST55454.2022.9827724
Grosz SA, Engelsma JJ, Liu E, Jain AK. C2CL: Contact to Contactless Fingerprint Matching. IEEE Trans. Inf. Forensics Secur. 2021 Dec 10; 17: 196-210. https://doi.org/10.1109/TIFS.2021.3134867
Chowdhury AMM, Imtiaz MH. Contactless Fingerprint Recognition Using Deep Learning—A Systematic Review. J. Cybersecur. Priv. 2022; 2(3): 714-730. https://doi.org/10.3390/jcp2030036
Priesnitz J, Rathgeb C, Buchmann N, Busch C, Margraf M. An overview of touchless 2D fingerprint recognition. EURASIP J Image Video Process. 2021 Dec; 2021: 1-28. https://doi.org/10.1186/s13640-021-00548-4
Krish RP, Fierrez J, Ramos D, Alonso-Fernandez F, Bigun J. Improving automated latent fingerprint identification using extended minutia types. Inf Fusion. 2019; 50. https://doi.org/10.1016/j.inffus.2018.10.001
Stein C, Nickel C, Busch C. Fingerphoto recognition with smartphone cameras. In: Proceedings of the International Conference of the Biometrics Special Interest Group, BIOSIG 2012. 2012.
Tiwari K, Gupta P. A touch-less fingerphoto recognition system for mobile hand-held devices. In: Proceedings of 2015 International Conference on Biometrics, ICB 2015. Institute of Electrical and Electronics Engineers Inc.; 2015. p. 151–6. https://doi.org/10.1109/ICB.2015.7139045
Birajadar P, Gupta S, Shirvalkar P, Patidar V, Sharma U, Naik A, et al. Touch-less fingerphoto feature extraction, analysis and matching using monogenic wavelets. In: 2016 International Conference on Signal and Information Processing, IConSIP 2016. 2017. https://doi.org/10.1109/ICONSIP.2016.7857436
Carney LA, Kane J, Mather JF, Othman A, Simpson AG, Tavanai A, et al. A multi-finger touchless fingerprinting system: Mobile fingerphoto and legacy database interoperability. In: ACM International Conference Proceeding Series. 2017. https://doi.org/10.1145/3168776.3168800
Deb D, Chugh T, Engelsma J, Cao K, Nain N, Kendall J, et al. Matching Fingerphotos to Slap Fingerprint Images. arXiv preprint arXiv: 1804.08122. 2018 Apr 22. https://doi.org/10.48550/arXiv.1804.08122
Al-Nima RR, Hasan SQ, Esmail S. Exploiting the deep learning with fingerphotos to recognize people. IJAST . 2020 Jul; 29(7): 13035-46.
Raid Rafi Omar Al-Nima. Signal Processing and Machine Learning Techniques for Human Verification Based on Finger Textures. PhD dissertation, Newcastle University, UK 2017;p. 1–195.
Asroni A, Ku-Mahamud KR, Damarjati C, Slamat HB. Arabic speech classification method based on padding and deep learning neural network. Baghdad Sci J 2021 Jun 20; 18(2 (Suppl.)): 0925-. https://doi.org/10.21123/bsj.2021.18.2(Suppl.).0925
Alajanbi M, Malerba D, Liu H. Distributed Reduced Convolution Neural Networks. MJBD. 2021; 2021 Jul 30; 2021: 25-8. https://doi.org/10.58496/MJBD/2021/005
Ardakani A, Condo C, Ahmadi M, Gross WJ. An Architecture to Accelerate Convolution in Deep Neural Networks. IEEE Transactions on Circuits and Systems I: Regular Papers. 2018; 65(4). https://doi.org/10.1109/TCSI.2017.2757036
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Adv Neural Inf Process Syst. 2012; 1097–105. https://doi.org/10.1145/3065386
Wu J. Introduction to Convolutional Neural Networks. Introduction to Convolutional Neural Networks. Introduction to Convolutional Neural Networks. 2017.
Al-Kaltakchi MT, Omar RR, Abdullah HN, Han T, Chambers JA. Finger Texture Verification Systems Based on multiple spectrum Lighting Sensors with Four Fusion Levels. IJICT. 2019;1(3). https://doi.org/10.31987/ijict.1.3.28
Ali SO, Al-Nima RRO, Mohammed EA. Individual Recognition with Deep Earprint Learning. In: International Conference on Communication and Information Technology. ICICT 2021. 2021. https://doi.org/10.1109/ICICT52195.2021.9568410
Ali SO, Al-Nima RRO, Mohammed EA. Communication establishment based on authenticating earprints. Int. J Future Gener Commun Netw. 2021; 14(1): 3242–6. https://doi.org/10.1109/ICICT52195.2021.9568410