Automatic latent fingerprint identification system using scale and rotation invariant minutiae features

2020 ◽  
Author(s):  
Uttam U. Deshpande ◽  
V. S. Malemath ◽  
Shivanand M. Patil ◽  
Sushma V. Chaugule
Keyword(s):  
Fingerprint Identification ◽  
Identification System ◽  
Rotation Invariant ◽  
Latent Fingerprint

Latent Fingerprint Identification System Based on a Local Combination of Minutiae Feature Points

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Uttam U. Deshpande ◽  
V. S. Malemath ◽  
Shivanand M. Patil ◽  
Sushma. V. Chaugule
Keyword(s):  
Fingerprint Identification ◽  
Identification System ◽  
Feature Points ◽  
Latent Fingerprint

The Use of Fingerprints in Identification

1979 ◽  
Vol 19 (4) ◽  
pp. 217-224 ◽  
Author(s):  
Commander G.T. C. Lambourne
Keyword(s):  
Fingerprint Identification ◽  
Identification System ◽  
Latent Fingerprint ◽  
Recent Developments ◽  
Fingerprint Data

This paper describes the introduction, progression and use of the fingerprint identification system. Recent developments in latent fingerprint retrieval and the computerized handling of fingerprint data are also detailed.

Automated latent fingerprint identification system: A review

2020 ◽  
Vol 309 ◽  
pp. 110187 ◽  
Author(s):  
Nancy Singla ◽  
Manvjeet Kaur ◽  
Sanjeev Sofat
Keyword(s):  
Fingerprint Identification ◽  
Identification System ◽  
Latent Fingerprint ◽  
System A

scholarly journals Latent Fingerprint Identification Using Deep Learning Method

2019 ◽  
pp. 497-503
Author(s):  
Shree Nandhini. P
Keyword(s):  
Deep Learning ◽  
Quality Assessment ◽  
Region Of Interest ◽  
Quality Metrics ◽  
Error Rates ◽  
Fingerprint Identification ◽  
Identification System ◽  
Latent Fingerprint ◽  
Biometric Quality

Digital fingerprint is one of the most consistent modalities in up to date biometrics and hence has been broadly studied and deploy in real applications. The accuracy of one Automatic Fingerprint Identification System (AFIS) largely depends on the quality of fingerprint samples, as it has an important impact on the degradation of the matching (comparison) error rates. This thesis generally focuses on the evaluation of biometric quality metrics and Fingerprint Quality Assessment (FQA), particularly in estimating the quality of gray-level latent fingerprint images or represented by minutiae set. By making a refined review of both biometric systems and relevant evaluation techniques, this contribute by the definition of a new evaluation or validation outline for estimating the performance of biometric quality metrics. It is defined to check the quality of latent fingerprint images by statistically measured parameters. In this work, an automatic Region-Of-Interest (ROI)-based latent fingerprint quality assessment technique is proposed by using deep learning. The first stage in our model uses deep learning, namely Region Convolutional Neural Network (R-CNN) to segment a latent fingerprint. In the second stage, feature vectors computed from the segmented latent fingerprint are used as input to a multi-class perceptron that predicts the value of the fingerprint. This proposed approach eliminates the need for manual ROI and feature markup by dormant examiners. Finally, experimental results on NIST SD27 show the effectiveness of our technique in latent fingerprint quality prediction

Pegasus: a distributed and load-balancing fingerprint identification system

2016 ◽  
Vol 17 (8) ◽  
pp. 766-780 ◽  
Author(s):  
Yun-xiang Zhao ◽  
Wan-xin Zhang ◽  
Dong-sheng Li ◽  
Zhen Huang ◽  
Min-ne Li ◽  
...  
Keyword(s):  
Load Balancing ◽  
Fingerprint Identification ◽  
Identification System

Fingerprint Identification System Based on DSP

2012 ◽  
Vol 468-471 ◽  
pp. 920-923
Author(s):  
Ya Ping Bao ◽  
Li Liu ◽  
Yuan Wang ◽  
Qian Song
Keyword(s):  
Software Design ◽  
High Speed ◽  
High Efficiency ◽  
Low Cost ◽  
Fingerprint Identification ◽  
Identification System ◽  
System Operation ◽  
Operation Speed ◽  
High Speed Data Acquisition ◽  
High Speed Data

This paper introduced a fast fingerprint identification system based on TMS320VC5416 DSP chip and MBF200 solidity fingerprint sensor. It precipitates fingerprint identification device developing into the direction of miniaturization, embedded and automatic.It recommends fingerprint identification system hardware and software design and the main system processing flow, aim at fingerprint identification arithmetic, the influence of system operation speed is being researched at the same time. High-speed data acquisition system is been built in order to achieve a DSP fingerprint identification system with high efficiency and low cost.

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