scholarly journals Latent Fingerprint Enhancement Using Tripolyphosphate-Chitosan Microparticles

2013 ◽  
Vol 2013 ◽  
pp. 1-4 ◽  
Author(s):  
Issa M. A. Il Dueik ◽  
Gordon A. Morris
Keyword(s):  
Drug Delivery ◽  
Fingerprint Identification ◽  
Latent Fingerprints ◽  
Latent Fingerprint ◽  
Ionotropic Gelation ◽  
Chitosan Microparticles ◽  
Fingerprint Enhancement

Chitosan has been widely used in the preparation of microparticles for drug delivery; however, it has not been considered in forensic applications. Tripolyphosphate- (TPP-) chitosan microparticles were formed using ionotropic gelation in the presence of a coloured dye and deposited onto latent fingerprints enabling fingerprint identification.

Biopolymers with Medical Applications Optimized method for obtaining chitosan-based delivery systems

2018 ◽  
Vol 69 (7) ◽  
pp. 1756-1759 ◽  
Author(s):  
Luminita Confederat ◽  
Iuliana Motrescu ◽  
Sandra Constantin ◽  
Florentina Lupascu ◽  
Lenuta Profire
Keyword(s):  
Drug Delivery ◽  
Drug Delivery Systems ◽  
Average Diameter ◽  
Delivery Systems ◽  
Cross Linking ◽  
Low Molecular Weight ◽  
Polymeric Systems ◽  
Chitosan Microparticles ◽  
Degree Of Swelling

The aim of this study was to optimize the method used for obtaining microparticles based on chitosan � a biocompatible, biodegradable, and nontoxic polymer, and to characterize the developed systems. Chitosan microparticles, as drug delivery systems were obtained by inotropic gelation method using pentasodiumtripolyphosphate (TPP) as cross-linking agent. Chitosan with low molecular weight (CSLMW) in concentration which ranged between 0.5 and 5 %, was used while the concentration of cross-linking agent ranged between 1 and 5%. The characterization of the microparticles in terms of shape, uniformity and adhesion was performed in solution and dried state. The size of the microparticles and the degree of swelling were also determined. The structure and the morphology of the developed polymeric systems were analyzed by Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).The average diameter of the chitosan microparticles was around 522 �m. The most stable microparticles were obtained using CSLMW 1% and TPP 2% or CSLMW 0.75%and TPP 1%. The micropaticles were spherical, uniform and without flattening. Using CSLMW in concentration of 0.5 % poorly cross-linked and crushed microparticles have been obtained at all TPP concentrations. By optimization of the method, stable chitosan-based micropaticles were obtained which will be used to develop controlled release systems for drug delivery.

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

scholarly journals Novel organic–inorganic hybrid powder SrGa12O19:Mn2+–ethyl cellulose for efficient latent fingerprint recognition via time-gated fluorescence

RSC Advances ◽  
2020 ◽  
Vol 10 (14) ◽  
pp. 8233-8243
Author(s):  
Jun'an Lai ◽  
Zhangwen Long ◽  
Jianbei Qiu ◽  
Dacheng Zhou ◽  
Qi Wang ◽  
...  
Keyword(s):  
Ethyl Cellulose ◽  
Fingerprint Recognition ◽  
Latent Fingerprints ◽  
Inorganic Hybrid ◽  
Latent Fingerprint ◽  
Efficient Strategy

A easy and efficient strategy for latent fingerprints recognition was developed by this work.

scholarly journals End-to-End Automated Latent Fingerprint Identification With Improved DCNN-FFT Enhancement

2020 ◽  
Vol 7 ◽  
Author(s):  
Uttam U. Deshpande ◽  
V. S. Malemath ◽  
Shivanand M. Patil ◽  
Sushma V. Chaugule
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Deep Convolutional Neural Network ◽  
Fingerprint Identification ◽  
Fingerprint Matching ◽  
Latent Fingerprint ◽  
Minutiae Extraction ◽  
End To End ◽  
Enhancement Algorithm

Automatic Latent Fingerprint Identification Systems (AFIS) are most widely used by forensic experts in law enforcement and criminal investigations. One of the critical steps used in automatic latent fingerprint matching is to automatically extract reliable minutiae from fingerprint images. Hence, minutiae extraction is considered to be a very important step in AFIS. The performance of such systems relies heavily on the quality of the input fingerprint images. Most of the state-of-the-art AFIS failed to produce good matching results due to poor ridge patterns and the presence of background noise. To ensure the robustness of fingerprint matching against low quality latent fingerprint images, it is essential to include a good fingerprint enhancement algorithm before minutiae extraction and matching. In this paper, we have proposed an end-to-end fingerprint matching system to automatically enhance, extract minutiae, and produce matching results. To achieve this, we have proposed a method to automatically enhance the poor-quality fingerprint images using the “Automated Deep Convolutional Neural Network (DCNN)” and “Fast Fourier Transform (FFT)” filters. The Deep Convolutional Neural Network (DCNN) produces a frequency enhanced map from fingerprint domain knowledge. We propose an “FFT Enhancement” algorithm to enhance and extract the ridges from the frequency enhanced map. Minutiae from the enhanced ridges are automatically extracted using a proposed “Automated Latent Minutiae Extractor (ALME)”. Based on the extracted minutiae, the fingerprints are automatically aligned, and a matching score is calculated using a proposed “Frequency Enhanced Minutiae Matcher (FEMM)” algorithm. Experiments are conducted on FVC2002, FVC2004, and NIST SD27 latent fingerprint databases. The minutiae extraction results show significant improvement in precision, recall, and F1 scores. We obtained the highest Rank-1 identification rate of 100% for FVC2002/2004 and 84.5% for NIST SD27 fingerprint databases. The matching results reveal that the proposed system outperforms state-of-the-art systems.

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