Process of fingerprint authentication using cancelable biohashed template

• Autorzy: Mamatha K. R. , Radhika K. R.

Identyfikatory

DOI

10.7494/csci.2023.24.4.4986

• Języki publikacji: EN

Abstrakty

EN

Template protection using cancelable biometrics prevents data loss and hacking stored templates, by providing considerable privacy and security. Hashingand salting techniques are used to build resilient systems. Salted password method is employed to protect passwords against different types of attacksnamely brute-force attack, dictionary attack, rainbow table attacks. Salting claims that random data can be added to input of hash function to ensure unique output. Hashing are speed bumps in an attacker’s road to breach user’s data. Research proposes a contemporary two factor authenticator called Biohashing. Biohashing procedure is implemented by recapitualted inner productover a pseudo random number generator key, as well as fingerprint features that are a network of minutiae. Cancelable template authentication used infingerprint-based sales counter accelerates payment process. Fingerhash is code produced after applying biohashing on fingerprint. Fingerhash is a binary string procured by choosing individual bit of sign depending on a preset threshold. Experiment is carried using benchmark FVC 2002 DB1 dataset. Authentication accuracy is found to be nearly 97%. Results compared with state-of-art approaches finds promising.

Słowa kluczowe

EN

biohashing; cancelable biometric; feature transformation; fingerprint

• Wydawca: Wydawnictwa AGH
• Czasopismo: Computer Science
• Rocznik: 2023
• Tom: T. 24 (4)
• Strony: 537--564
• Opis fizyczny: Bibliogr. 28 poz., rys., tab., wykr.

Twórcy

autor

Mamatha K. R.

• Visvesvaraya Technological University, B.M.S. College of Engineering, Bangalore

autor

Radhika K. R.

• Visvesvaraya Technological University, B.M.S. College of Engineering, Bangalore

Bibliografia

• [1] Abdullahi S.M., Wang H., Li T.: Fractal Coding-Based Robust and Alignment-Free Fingerprint Image Hashing,IEEE Transactions on Information Forensicsand Security, vol. 15, pp. 2587–2601, 2020. doi: 10.1109/TIFS.2020.2971142.
• [2] Belguechi R., Rosenberger C., Ait-Aoudia S.: Biohashing for Securing Minutiae Template. In:2010 20th International Conference on Pattern Recognition, pp. 1168–1171, 2010. doi: 10.1109/ICPR.2010.292.
• [3] Bychkov O., Merkulova K., Zhabska Y.: Information Technology of Person?s Identification by Photo Portrait. In:2020 IEEE 15th International Conferenceon Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET), pp. 786–790, 2020. doi: 10.1109/TCSET49122.2020.235542.
• [4] Chang D., Garg S., Hasan M., Mishra S.: Cancelable Multi-Biometric Approach Using Fuzzy Extractor and Novel Bit-Wise Encryption, IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3152–3167, 2020. doi: 10.1109/TIFS.2020.2983250.
• [5] Fuksis R., Kadikis A., Greitans M.: Biohashing and Fusion of Palmprint and Palm Vein Biometric Data. In:2011 International Conference on Hand-Based Biometrics, pp. 1–6, 2011. doi: 10.1109/ICHB.2011.6094334.
• [6] Hashad F.G., Zahran O., El-Rabaie S., Elashry I.F., Elbanby G., Dessouky M.I.,El-Samie A.F.E.: Cancelable Fingerprint Recognition based on Encrypted Convolution Kernel in Different Domains, Menoufia Journal of Electronic Engineering Research, vol. 29(2), pp. 133–142, 2020. doi: 10.21608/mjeer.2020.103957.
• [7] Im J.H., Jeon S.Y., Lee M.K.: Practical Privacy-Preserving Face Authentication for Smartphones Secure Against Malicious Clients, IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2386–2401, 2020. doi: 10.1109/TIFS.2020.2969513.
• [8] Jin A.T.B., Ling D.N.C., Goh A.: Biohashing: two factor authentication featuring fingerprint data and tokenised random number, Pattern Recognition, vol. 37(11), pp. 2245–2255, 2004. doi: 10.1016/j.patcog.2004.04.011.
• [9] Kho J.B., Kim J., Kim I.J., Teoh A.B.: Cancelable fingerprint template design with randomized non-negative least squares, Pattern Recognition, vol. 91,pp. 245–260, 2019. doi: 10.1016/j.patcog.2019.01.039.
• [10] Kim J., Kang P.: Freely typed keystroke dynamics-based user authentication formobile devices based on heterogeneous features, Pattern Recognition, vol. 108,107556, 2020. doi: 10.1016/j.patcog.2020.107556.
• [11] Kong A., Cheung K.H., Zhang D., Kamel M., You J.: An analysis of Bio-Hashing and its variants, Pattern Recognition, vol. 39(7), pp. 1359–1368, 2006.doi: 10.1016/j.patcog.2005.10.025.
• [12] Kuzu R.S., Piciucco E., Maiorana E., Campisi P.: On-the-Fly Finger-Vein-Based Biometric Recognition Using Deep Neural Networks, IEEE Transactions on Information Forensics and Security, vol. 15, pp. 2641–2654, 2020.doi: 10.1109/TIFS.2020.2971144.
• [13] Lai Y., Jin Z., Wong K., Tistarelli M.: Efficient Known-Sample Attack for Distance-Preserving Hashing Biometric Template Protection Schemes, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3170–3185, 2021.doi: 10.1109/TIFS.2021.3073802.
• [14] Li Y., Zhao H., Cao Z., Liu E., Pang L.: Compact and Cancelable Fingerprint Binary Codes Generation via One Permutation Hashing, IEEE Signal Processing Letters, vol. 28, pp. 738–742, 2021. doi: 10.1109/LSP.2021.3071262.
• [15] Liu C., Yang Y., Liu X., Fang L., Kang W.: Dynamic-Hand-Gesture Authentication Dataset and Benchmark, IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1550–1562, 2021. doi: 10.1109/TIFS.2020.3036218.
• [16] Lumini A., Nanni L.: An improved BioHashing for human authentication, Pattern Recognition, vol. 40(3), pp. 1057–1065, 2007. doi: 10.1016/j.patcog.2006.05.030.
• [17] Nagar A., Nandakumar K., Jain A.K.: Biometric template transformation: a security analysis. In: Media Forensics and Security II, vol. 7541, 75410O, International Society for Optics and Photonics, 2010. doi: 10.1117/12.839976.
• [18] Okawa M.: Time-series averaging and local stability-weighted dynamic time warping for online signature verification, Pattern Recognition, vol. 112, 107699,2021. doi: 10.1016/j.patcog.2020.107699.
• [19] Pratap T., Kokil P.: Approximate Optimization of Gabor Filter Parameters in Application to Blood Vessel Segmentation in Retinal Images. In: 2019 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), pp. 1–5, 2019. doi: 10.1109/WIECON-ECE48653.2019.9019964.
• [20] Sadhya D., Akhtar Z., Dasgupta D.: A Locality Sensitive Hashing Based Approach for Generating Cancelable Fingerprints. In: 2019 IEEE 10th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1–9,2019. doi: 10.1109/BTAS46853.2019.9185991.
• [21] Shahreza H.O., Marcel S.: Towards Protecting and Enhancing Vascular Biometric Recognition Methods via Biohashing and Deep Neural Networks, IEEE Transactions on Biometrics, Behavior, and Identity Science, vol. 3(3), pp. 394–404,2021. doi: 10.1109/TBIOM.2021.3076444.
• [22] Shahzad M., Wang S., Deng G., Yang W.: Alignment-free cancelable fingerprint templates with dual protection, Pattern Recognition, vol. 111, 107735, 2021.doi: 10.1016/j.patcog.2020.107735.
• [23] Teoh A.B., Ngo D.C.: Cancellable biometerics featuring with tokenised random number, Pattern Recognition Letters, vol. 26(10), pp. 1454–1460, 2005.doi: 10.1016/j.patrec.2004.11.021.
• [24] Vhaduri S., Poellabauer C.: Multi-Modal Biometric-Based Implicit Authentication of Wearable Device Users, IEEE Transactions on Information Forensics and Security, vol. 14(12), pp. 3116–3125, 2019. doi: 10.1109/TIFS.2019.2911170.
• [25] Wang S., Hu J.: A blind system identification approach to cancelable fingerprint templates, Pattern Recognition, vol. 54, pp. 14–22, 2016. doi: 10.1016/j.patcog.2016.01.001.
• [26] Zhang J., Fang P.: Finger Vein Template Encryption Scheme Based on BioHashing. In: 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), pp. 681–685, 2018. doi: 10.1109/SDPC.2018.8664820.
• [27] Zheng Y., Cao Y., Chang C.H.: Facial biohashing based user-device physical unclonable function for bring your own device security. In: 2018 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–6, 2018. doi: 10.1109/ICCE.2018.8326074.
• [28] Zhu T., Fu L., Liu Q., Lin Z., Chen Y., Chen T.: One Cycle Attack: Fool Sensor-Based Personal Gait Authentication With Clustering, IEEE Transactionson Information Forensics and Security, vol. 16, pp. 553–568, 2021. doi: 10.1109/TIFS.2020.3016819.