Biometrics based on deep learning: A survey

Document Type : Research Paper

Authors

1 College of Science, University of Diyala, Baqubah, Iraq

2 College of Engineering, University of Diyala, Baqubah, Iraq

Abstract

Biometrics is concerned with the identification and verification of people using their physiological and behavioral characteristics which are enduring and distinctive and these characteristics can distinguish one person from another. Systems for biometric recognition integrate intricate technological, operational, and definitional choices in a variety of scenarios. These systems combined with biometric strategies and authentication techniques will help to enhance the security of applications that rely on user collaboration. They aid in locating a specific person inside a set of industrial networks, office buildings, and control systems. This paper focused on convolutional neural networks, deep learning in biometrics, Unimodal and Multimodal Biometrics, template security, and general challenges of biometrics. This article examines a comprehensive and in-depth survey that succinctly and methodically on fingerprint and vein biometrics, analyzed, and compared to determine which is more effective in verifying the identification of a specific user and to highlight a biometric authentication system and the challenges of biometrics. We discuss each method and dataset used as well as their efficacy. The key difficulties in using these biometric recognition models as well as prospective directions for future study in this area are also covered.

Keywords

[1] C. Affonso, A.L.D. Rossi, F.H.A. Vieira, and A.C.P. de Leon Ferreira, Deep learning for biological image classification, Expert Syst. Appl. 85 (2017), 114–122.
[2] D.M Ahmed, S.Y. Ameen, N. Omar, S.F. Kak, Z.N. Rashid, H.M. Yasin, I.M. Ibrahim, A.A. Salih, N.O. Salim, and A.M. Ahmed, A state of art for survey of combined iris and fingerprint recognition systems, Asian J. Res. Comput. Sci. 10 (2021), no. 1, 18–33.
[3] S. Ahmad Radzi, M. Khalil-Hani, and R. Bakhteri, Finger-vein biometric identification using convolutional neural network, Turkish J. Electr. Eng. Comput. Sci. 24 (2016), no. 3, 1863–1878.
[4] M. Ahsan, M.A. Based, J. Haider, and M. Kowalski, An intelligent system for automatic fingerprint identification using feature fusion by Gabor filter and deep learning, Comput. Electr. Eng. 95 (2021), 107387.
[5] M.M.H. Ali, V.H. Mahale, P. Yannawar, and A.T. Gaikwad, Overview of fingerprint recognition system, Int. Conf. Electr. Electron. Optim. Tech. ICEEOT 2016, pp. 1334–1338.
[6] S. Almabdy and L. Elrefaei, Deep convolutional neural network-based approaches for face recognition, Appl. Sci. (Switzerland) 9 (2019), no. 20.
[7] D.L. Andreea-Monica, S. Moldovanu, and L. Moraru, A fingerprint matching algorithm using the combination of edge features and convolution neural networks, Inventions 7 (2022), no. 2, 1–13.
[8] S. Anton, T. Artem, P. Andrey, and K. Igor, Modification of VGG neural network architecture for unimodal and multimodal biometrics, IEEE East-West Design and Test Symposium, EWDTS Proc., 2020, pp. 1–4.
[9] A. Avci, M. Kocakulak, and N. Acir, Convolutional neural network designs for finger-vein-based biometric identification, ELECO 11th Int. Conf. Electr. Electron. Eng., 2019, pp. 580–584.
[10] B. Bakhshi and H. Veisi, End-to-end fingerprint verification based on convolutional neural network, ICEE 27th Iran. Conf. Electr. Eng., 2019, pp. 1994–1998.
[11] M.M. Bejani and M. Ghatee, A systematic review on overfitting control in shallow and deep neural networks, Artif. Intell. Rev. 54 (2021), 6391–6438.
[12] T.R. Borah, K.K. Sarma, and P.H. Talukdar, Retina recognition system using adaptive neuro-fuzzy inference system, IEEE Int. Conf. Comput. Commun. Control. IC4 2015, 2016, pp. 1–6.
[13] I. Boucherit, M. Ould, H. Hentabli, and B. Affendi, Finger vein identification using deeply-fused Convolutional Neural Network, J. King Saud Univ. Comput. Inf. Sci. 34 (2022), no. 3, 646–656.
[14] L. Cai, J. Gao, and D. Zhao, A review of the application of deep learning in medical image classification and segmentation, Ann. Transl. Medicine 8 (2020), no. 11, 713–713.
[15] E. Cherrat, R. Alaoui, and H. Bouzahir, Convolutional neural networks approach for multimodal biometric identification system using the fusion of fingerprint, finger-vein and face images, PeerJ Comput. Sci. 2020 (2020), no. 1, 1–15.
[16] T.Z. Chin, A. Saidatul, and Z. Ibrahim, Exploring EEG based authentication for imaginary and non-imaginary tasks using power spectral density method, IOP Conf. Ser. Mater. Sci. Eng. 557 (2019), no. 1, 012031.
[17] A.M.M. Chowdhury and M.H. Imtiaz, Contactless fingerprint recognition using deep learning & mdash: A systematic review, J. Cybersecurity Priv. 2 (2022), no. 3, 714–730.
[18] M. Choudhary, V. Tiwari, and U. Venkanna, Iris anti-spoofing through score-level fusion of handcrafted and data-driven features, Appl. Soft Comput. J. 91 (2020).
[19] S. Dargan and M. Kumar, A comprehensive survey on the biometric recognition systems based on physiological and behavioral modalities, Expert Syst. Appl. 143 (2020), 113114.
[20] L.M. Dinca and G.P. Hancke, The fall of one, the rise of many: A survey on multi-biometric fusion methods, IEEE Access 5 (2017), 6247–6289.
[21] K. Fatima, S. Nawaz and S. Mehrban, Biometric authentication in health care sector: A survey, 3rd Int. Conf. Innov. Comput. ICIC, 2019, pp. 1–10.
[22] P.L. Gald´amez, W. Raveane, and A.G. Arrieta, A brief review of the ear recognition process using deep neural networks, J. Appl. Log. 24 (2017), 62–70.
[23] U. Gawande and Y. Golhar, Biometric security system: A rigorous review of unimodal and multimodal biometrics techniques, Artic. Int. J. Biomet. 10 (2018), no. 2, 142–175.
[24] D. Gumusbas, T. Yildirim, M. Kocakulak, and N. Acir, Capsule network for finger-vein-based biometric identification, IEEE Symp. Ser. Comput. Intell. SSCI, 2019, pp. 437–441.
[25] S. Haware and A. Barhatte, Retina-based biometric identification using SURF and ORB feature descriptors, Int. Conf. Microelectron. Devices, Circuits Syst. ICMDCS, 2017, pp. 1–6.
[26] W. Jian, Y. Zhou, and H. Liu, Lightweight convolutional neural network based on singularity ROI for fingerprint classification, IEEE Access 8 (2020), 54554–54563.
[27] M. Karakaya and E.T. Celik, Effect of pupil dilation on off-angle iris recognition, J. Electr. Imaging, 28 (2019), no. 3, 033022.
[28] N. Kaur, A study of biometric identification and verification system, Int. Conf. Adv. Comput. Innov. Technol. Eng. ICACITE, 2021, pp. 60–64.
[29] I. Kovac and P. Marak, Openfinger: Towards a combination of discriminative power of fingerprints and finger vein patterns in multimodal biometric system, Tatra Mt. Math. Publ. 77 (2020), no. 1, 109–138.
[30] Y. Li, Research and application of deep learning in image recognition, IEEE 2nd Int. Conf. Power, Electron. Comput. Appl. ICPECA, 2022, pp. 994–999.
[31] C. Lin and A. Kumar, Matching contactless and contact-based conventional fingerprint images for biometrics identification, IEEE Trans. Image Process. 27 (2018), no. 4, 2008–2021.
[32] J. Mason, R. Dave, P. Chatterjee, I. Graham-Allen, A. Esterline, and K. Roy, An investigation of biometric authentication in the healthcare environment, Array 8 (2020), 100042.
[33] H. Mehraj and A.H. Mir, A survey of biometric recognition using deep learning, EAI Endorsed Trans. Energy Web 8 (2021), no. 33, 1–16.
[34] P. Melzi, C. Rathgeb, R. Tolosana, R. Vera-Rodriguez, and C. Busch, An overview of privacy-enhancing technologies in biometric recognition, arXiv preprint arXiv:2206.10465, (2022).
[35] G. Meng, P. Fang, and B. Zhang, Finger vein recognition based on convolutional neural network, MATEC Web Conf., 2017.
[36] S. Minaee, E. Azimi and A. Abdolrashidi, FingerNet: pushing the limits of fingerprint recognition using convolutional neural network, arXiv preprint arXiv:1907.12956, (2019).
[37] J.C. Moreno-Rodriguez, J.C. Atenco-Vazquez, J.M. Ramirez-Cortes, R. Arechiga-Martinez, P. Gomez-Gil, and R. Fonseca-Delgado, BIOMEX-DB: A cognitive audiovisual dataset for unimodal and multimodal biometric systems, IEEE Access 9 (2021), 111267–111276.
[38] S.M.M. Najeeb, R.R.O. Al-Nima, and M.L. Al-Dabag, Reinforced deep learning for verifying finger veins, Int. J. online Biomed. Eng. 17 (2021), no. 7, 19–27.
[39] K.J. Noh, J. Choi, J.S. Hong, and K.R. Park, Finger-vein recognition based on densely connected convolutional network using score-level fusion with shape and texture images, IEEE Access 8 (2020), 96748–96766.
[40] M.O. Oloyede and G.P. Hancke, Unimodal and multimodal biometric sensing systems: A review, IEEE Access 4 (2016), 7532–7555.
[41] M. Pak and S. Kim, A review of deep learning in image recognition, Proc. 4th Int. Conf. Comput. Appl. Inf. Process. Technol. CAIPT 2017, 2018, pp. 1–3.
[42] B. Pandya, G. Cosma, A.A. Alani, and A. Taherkhani, Fingerprint classification using a deep convolutional neural network, 4th IEEE Int. Conf. Inf. Manag., 2018, pp. 86–91.
[43] J. Priesnitz, C. Rathgeb, N. Buchmann, C. Busch, and M. Margraf, An overview of touchless 2D fingerprint recognition, EURASIP J. Image Video Process. 2021 (2021), 8.
[44] H. Qin and P. Wang, Finger-vein verification based on LSTM recurrent neural networks, Appl. Sci. 9 (2019), no. 8, 1–18.
[45] J. Ribeiro Pinto, J.S. Cardoso, and A. Lourenco, Evolution, current challenges, and future possibilities in ECG Biometrics, IEEE Access 6 (2018), 34746–34776.
[46] T. Sabhanayagam, V.P. Venkatesan and K. Senthamaraikannan, A comprehensive survey on various biometric systems, Int. J. Appl. Eng. Res. 13 (2018), no. 5, 2276–2297.
[47] F. Saeed, M. Hussain, and H.A. Aboalsamh, Automatic fingerprint classification using deep learning technology (DeepFKTNet), Math. 10 (2022), no. 8, 1285.
[48] P. Saikia, R.D. Baruah, S.K. Singh, and P.K. Chaudhuri, Artificial neural networks in the domain of reservoir characterization: A review from shallow to deep models, Comput. Geosci. 135 (2020), 104357.
[49] R. Saini, B. Kaur, P. Singh, P. Kumar, and P.P. Roy, Don’t just sign use brain too: A novel multimodal approach for user identification and verification, Inf. Sci. 430 (2018), 163–178.
[50] K. Shaheed, H. Liu, G. Yang, I. Qureshi, J. Gou, and Y. Yin, A systematic review of finger vein recognition techniques, Inf. 9 (2018), no. 9, 213.
[51] M. Sharif, M. Raza, J.H. Shah, M. Yasmin, and S.L. Fernandes, An Overview of Biometrics Methods, Handbook of Multimedia Information Security: Techniques and Applications, 2019.
[52] S.A. Shawkat, K.S.L. Al-Badri, and A.I. Turki, The new hand geometry system and automatic identification, Period. Engin. Natural Sci. 7 (2019), no. 3, 996–1008.
[53] K. Siddique, Z. Akhtar, and Y. Kim, Biometrics vs passwords: A modern version of the tortoise and the hare, Comput. Fraud Secur. 2017 (2017), no. 1, 13–17.
[54] G.K. Sidiropoulos, P. Kiratsa, P. Chatzipetrou, and G.A. Papakostas, Feature extraction for finger-vein-based identity recognition, J. Imag. 7 (2021), no. 5.
[55] M. Singh, R. Singh, and A. Ross, A comprehensive overview of biometric fusion, Inf. Fusion 52 (2019), 187–205.
[56] S. Socheat and T. Wang, Fingerprint enhancement, minutiae extraction and matching techniques, J. Comput. Commun. 8 (2020), no. 5, 55–74.
[57] J.M. Song, W. Kim, and K.R. Park, Finger-vein recognition based on deep densenet using composite image, IEEE Access 7 (2019), 66845–66863.
[58] A. Takahashi, Y. Koda, K. Ito, and T. Aoki, Fingerprint feature extraction by combining texture, minutiae, and frequency spectrum using multi-task CNN, IJCB IEEE/IAPR Int. Jt. Conf. Biometrics, 2020, pp. 1–8.
[59] L.D. Tamang and B.W. Kim, FVR-Net: Finger vein recognition with convolutional neural network using hybrid pooling, Appl. Sci. 12 (2022), no. 15, 7538.
[60] H.M. Therar, E.A. Mohammed and A.J. Ali, Biometric signature based public key security system, 3rd Int. Conf. Adv. Sci. Eng. ICOASE 2020, pp. 133–138.
[61] M. Wang and W. Deng, Deep face recognition: A survey, Neurocomput. 429 (2021), 215–244.
[62] K.N. Win, K. Li, J. Chen, P.F. Viger, and K. Li, Fingerprint classification and identification algorithms for criminal investigation: A survey, Futur. Gener. Comput. Syst. 110 (2020), 758–771.
[63] F. Wu, J. Zhu, and X. Guo, Fingerprint pattern identification and classification approach based on convolutional neural networks, Neural Comput. Appl. 32 (2020), no. 10, 5725–5734.
[64] W. Yang, S. Wang, J. Hu, G. Zheng, and C. Valli, A fingerprint and finger-vein based cancelable multi-biometric system, Pattern Recogn. 78 (2018), 242–251.
[65] J.C. Zapata, C.M. Duque, Y. Rojas-Idarraga, M.E. Gonzalez, J.A. Guzman, and M.A. Becerra Botero, Data fusion applied to biometric identification–A review, Communicat. Comput. Inf. Sci. 735 (2017), 721–733.
Volume 16, Issue 5
May 2025
Pages 43-55
  • Receive Date: 17 March 2024
  • Accept Date: 10 June 2024