This document discusses a proposed system for storing fingerprint biometric data on blockchain to provide more secure authentication. Currently, biometric data is stored in centralized databases controlled by a single authority, making it vulnerable to attacks. The proposed system would decentralize storage of fingerprint templates using blockchain and InterPlanetary File System (IPFS) to address these security issues. If implemented, the system could provide safe and convenient identification using biometrics while protecting users' fingerprint data.

1. FINGER PRINT RECOGANIZTION IN
BLOCKCHAIN
RECORD AUTHENTICATION
Presented by
• Avanthika S - 210919205009
• Karthik V - 210920205019
• Kishore I - 210920205021
• Vasanth K - 201920205055
• Loyola institute of technology

2. ABSTRACT
Biometrics, with its uniqueness to every individual, has been adapted as a security authentication
feature by many institutions. These biometric data are processed into templates that are saved on
databases, and a central authority centralizes and controls these databases. This form of storing
biometric data, or in our case fingerprint template, is asymmetric and prone to three main security
attacks, such as fake template input, template modification or deletion, and channel interception by a
malicious attacker.
Keywords:
blockchain;
IPFS;
fingerprint
hashing;
decentralization;
biometrics;
encryption

3. CONTENT
• Introduction
• Literature survey
• Example literature
• Exitsting system
• Disadvantages
• Architectural diagram
• proposed system
• Advantages
• Modules
• System recruitment

4. OBJECTIVE
To provide safe and convenient identification and authentication with a human touch.By
utilizing the unique properties of biometrics, Fingerprints’ solutions create an easier and more
secure life. The value contribution is based on a sensitivity to the need of the market,
employees with expertise, financial capital and good relations.

5. INTRODUCTION
• Biometric is the science of technology of measuring and analysing biological
data
• Biometrics are automated methods Roncognition person based on
physiological behave wear behaviour characteristics.
• As the level of security decrease And transition fraud increases, the need for
highly b identification and personal verification Technologies more
• Biometric Based solutions or able to provide for confidential transaction
for data privacy

6. LITERATURE SURVEY
• Those quality metrics can be simply summarised in several points: quality metrics
based on the orientation of fingerprint pattern; algorithms that rely on the variation of
Gabor responses; approaches in frequency domain; measurements based on pixel
information and quality indexes rely on classification with multi-feature. In addition, that
study also analysed quality metrics mainly in terms of the linearity between them.

7. EXAMPLE LITERATURE
• In this study, we classify the existing studies into several frameworks in terms
of their implementation to show the difference and some potential problems
that need to be considered. As mentioned above, the quality metrics that had
been proposed so far are all dependent on one or several features. According
to how they are carried out, this study categorises them as: (i) segmentation-
based approaches; (ii) a single feature-based quality index; and (iii) solutions
rely on a combination of multi-features or indexes, which is further divided
into methods based on linear fusion and classification

8. EXITING SYSTEM
• Banking solutions and the payment technologies available today use a wide
range of biometric modalities: fingerprints, iris, voice, face, fingerprint, palm
veins, behavior, and other types of biometric recognition are all used alone or
combined in a multifactorial manner as a system, to lock accounts and serve
against .

9. DISADVANTAGES
• Costs – Significant investment needed in biometrics for security.
• Data breaches – Biometric databases can still be hacked.
• Tracking and data – Biometric devices like facial recognition systems can limit
privacy for users.

10. ARCHITECTURAL DIAGRAM

11. PROPOSED SYSTEM
• Biometric data also need to be secured as any breach the biometric data can
result in rendering it permanently damaged and unusable . This is because the
moment a stored biometric data is compromised it is rendered insecure for
authentication purposes for the entire life of the user

12. ADVANTAGES
• High security and assurance – Biometric identification provides the answers to
“something a person has and is” and helps verify identity.
• User Experience – Convenient and fast.
• Non-transferrable – Everyone has access to a unique set of biometrics.

13. MODULES
• There are two types of fingerprint sensor module: optical and capacitive
• A fingerprint is an impression of the pattern of ridges on the last joint of a person’s
finger. Properties that make a fingerprint useful for identification are.
• (1) its unique, characteristic ridges;
• (2) its consistency over a person’s lifetime;
• The systematic classification used for fingerprints.

14. SYSTEM RECRUITMENT
• Fingerprint recognition systems work by examining a finger pressed
against a smooth surface. The finger’s ridges and valleys are scanned, and
a series of distinct points, where ridges and valleys end or meet, are called
minutiae. These minutiae are the points the fingerprint recognition
system uses for comparison.

15. REFERENCE
• W. Badler, “Dermatoglyphics: Sciencetransition, vol9, pp95, 1991.
• M. Kuchen, C. Newell, “A Model for fingerprint formation, “Europhysletters, vol.68, No.1,
pp.141-447, 2004.
• L. Hong, Y. Wang, A. K. Jain, Fingerprint image enhancement: Algorithm and performance
evaluation, Transactionson vol. PAMI21 No.4, pp.777-789, 1998
• S. Greenberg, M. Aladjem, D. Kogan, I. Dimitrov, Fingerprint image enhancement using
filtering techniques, in: International Conference on Pattern Recognition, Vol.3, pp.326-329,
2000.
• L.O’Gormann, J.V. Nickerson, Anapproachto fingerprint filter design, Pattern Recognition 22
No. 1, pp.29-38, 1989.