Kernel approach for similarity measure in latent fingerprint recognition

Kumar, Sachin; Velusamy, R. Leela

doi:10.1109/iceteeses.2016.7581411

Cited by 5 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Two experiments are performed in terms of aforementioned parameters for the performance evaluation of the proposed automated latent fingerprint matching system. The first experiment involves the analysis and comparison of the similarity score of proposed system with results achieved by Kumar & Velusamy [29]. In the second experiment, system's identification rate is compared with results assessed by Venkatesh et al [31].…”

Section: Resultsmentioning

confidence: 99%

An Optimised Latent Fingerprint Matching System Using Cuckoo Search

Jindal¹,

Singla²

2018

IJIES

View full text Add to dashboard Cite

Biometrics is a scientific automation based on behavioural and physiological human traits to identify a person uniquely. Fingerprints are one of the eminent human psychological cues with an admiral potential for person identification. It has been widely adopted by many law enforcement agencies, forensics for criminal investigation. Amongst the three categories of fingerprints, latent fingerprints are the most intricate prints found in crime scenes and functions as dominant evidence in forensic investigations. The development of an accurate and reliable automatic latent fingerprint system is one of the pivotal issues for fingerprint researchers. This paper seeks the development of an optimal system for accurate latent fingerprint matching. The proposed system includes preprocessing of latent fingerprint image to remove noisy or unwanted information followed by minutiae extraction. The final step of proposed system includes the employment of swarm intelligence based cuckoo search algorithm to acquire optimal matching results. The performance of the system is evaluated on NIST Special Database 27 in terms of similarity score and identification rate. For the three categories of NIST dataset good, bad & ugly, the proposed system has achieved similarity score value of 94.50%, 86.22% & 84.78% and identification rates of 97.72%, 87.05% & 83.52% respectively. The proposed system outperformed in comparison with other considered concept of latent fingerprint matching techniques.

show abstract

Section: Resultsmentioning

confidence: 99%

An Optimised Latent Fingerprint Matching System Using Cuckoo Search

Jindal¹,

Singla²

2018

IJIES

View full text Add to dashboard Cite

show abstract

“…For verification or authentication of user, the extracted fingerprint template from captured fingerprint image is compared with the reference fingerprint template stored in database. The Euclidean distance calculation is used for obtaining matching score based on the similarities [8] between two fingerprint templates. If the matching score is greater than the value of threshold (Th), the user is recognized (success or accepted).…”

Section: The Proposed Fingerprint Recognition and Referencementioning

confidence: 99%

Novel Adaptive Auto-Correction Technique for Enhanced Fingerprint Recognition

P¹,

Srikantaswamy²,

Manjunatha³

2019

IJACSA

View full text Add to dashboard Cite

Fingerprints are the most used biometric trait in applications where high level of security is required. Fingerprint image may vary due to various environmental conditions like temperature, humidity, weather etc. Hence, it is necessary to design a fingerprint recognition system that is robust against temperature variations. Existing techniques such as automated and non-automated techniques are not real time analysis (adaptive). In this paper, we propose an adaptive auto correction technique called Reference Auto-correction Algorithm. This proposed algorithm corrects user reference fingerprint template automatically based on captured fingerprint template and the matching score obtained on daily basis to improve the recognition rate. Analysis is carried out on 250 fingerprint templates stored in the database of 10-users captured at varying temperature from 25 0 C to 0 0 C. The experimental result shows 40% improvement in the recognition rate after applying auto correction algorithm.

show abstract

A Systematic Analysis of Fingerprint Matching Techniques for Fingerprint Recognition System

Kumar

2022

Innovations in Computer Science and Engineering

View full text Add to dashboard Cite

Kernel approach for similarity measure in latent fingerprint recognition

Cited by 5 publications

References 16 publications

An Optimised Latent Fingerprint Matching System Using Cuckoo Search

An Optimised Latent Fingerprint Matching System Using Cuckoo Search

Novel Adaptive Auto-Correction Technique for Enhanced Fingerprint Recognition

A Systematic Analysis of Fingerprint Matching Techniques for Fingerprint Recognition System

Contact Info

Product

Resources

About