An aggregation-induced emission luminogen combined with a cyanoacrylate fuming method for latent fingerprint analysis

The Analyst ◽  
2020 ◽  
Vol 145 (6) ◽  
pp. 2311-2318 ◽  
Author(s):  
Xiaodong Jin ◽  
Hao Wang ◽  
Ran Xin ◽  
Yining Ma ◽  
Guoping Wu ◽  
...  
Keyword(s):  
Fingerprint Analysis ◽  
Latent Fingerprints ◽  
Latent Fingerprint ◽  
Aggregation Induced Emission ◽  
Cyanoacrylate Fuming

An aggregation-induced emission luminogen tetraphenylethene-based dye FLA-2, was synthesized and characterized. Two methods were adopted herein to visualize the latent fingerprints, respectively.

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.

Phenyl-doped graphitic carbon nitride: photoluminescence mechanism and latent fingerprint imaging

Nanoscale ◽  
2017 ◽  
Vol 9 (45) ◽  
pp. 17737-17742 ◽  
Author(s):  
Zhiping Song ◽  
Zhihong Li ◽  
Lihua Lin ◽  
Yongfan Zhang ◽  
Tianran Lin ◽  
...  
Keyword(s):  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Latent Fingerprints ◽  
Latent Fingerprint ◽  
Photoluminescence Mechanism

Green-photoluminescent phenyl-doped graphitic carbon nitride (PDCN) powders were prepared and applied for the visualization of latent fingerprints.

A tetraphenylethene-based dye for latent fingerprint analysis

2017 ◽  
Vol 244 ◽  
pp. 777-784 ◽  
Author(s):  
Xiaodong Jin ◽  
Ran Xin ◽  
Shifan Wang ◽  
Wenzhu Yin ◽  
Tongxiang Xu ◽  
...  
Keyword(s):  
Fingerprint Analysis ◽  
Latent Fingerprint

scholarly journals Non-toxic luminescent Au Nanoclusters@Montmorillonite nanocomposites powders for latent fingerprint development

RSC Advances ◽  
2017 ◽  
Vol 7 (79) ◽  
pp. 50106-50112 ◽  
Author(s):  
Yanlin Yu ◽  
Lei Yan ◽  
Zhining Xia
Keyword(s):  
Latent Fingerprints ◽  
Au Nanoclusters ◽  
Latent Fingerprint ◽  
Latent Fingerprint Development ◽  
Montmorillonite Nanocomposites

Latent fingerprints visualized by using as-prepared AuNCs@MMT nanocomposites powders as a fluorescent developing reagent on a ceramic mug.

Refreshing the Aged Latent Fingerprints with Ionizing Radiation Prior to the Cyanoacrylate Fuming Procedure: A Preliminary Study

2015 ◽  
Vol 61 (3) ◽  
pp. 787-791 ◽  
Author(s):  
Mimoza M. Ristova ◽  
Pavlina Radiceska ◽  
Igorco Bozinov ◽  
Lambe Barandovski
Keyword(s):  
Ionizing Radiation ◽  
Latent Fingerprints ◽  
Cyanoacrylate Fuming ◽  
Preliminary Study

scholarly journals Latent Fingerprint Segmentation Based on Ridge Density and Orientation Consistency

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Manhua Liu ◽  
Shuxin Liu ◽  
Weiwu Yan
Keyword(s):  
Local Features ◽  
Poor Quality ◽  
Texture Image ◽  
Law Enforcement Agencies ◽  
Latent Fingerprints ◽  
Latent Fingerprint ◽  
Processing Step ◽  
Fingerprint Segmentation ◽  
Fingerprint Database ◽  
Global And Local

Latent fingerprints are captured from the fingerprint impressions left unintentionally at the surfaces of the crime scene. They are often used as an important evidence to identify criminals in law enforcement agencies. Different from the widely used plain and rolled fingerprints, the latent fingerprints are usually of poor quality consisting of complex background with a lot of nonfingerprint patterns and various noises. Latent fingerprint segmentation is an important image processing step to separate fingerprint foreground from background for more accurate and efficient feature extraction and matching. Traditional methods are usually based on the local features such as gray scale variance and gradients, which are sensitive to noise and cannot work well for latent images. This paper proposes a latent fingerprint segmentation method based on combination of ridge density and orientation consistency, which are global and local features of fingerprints, respectively. First, a texture image is obtained by decomposition of latent image with a total variation model. Second, we propose to detect the ridge segments from the texture image, and then compute the density of ridge segments and ridge orientation consistency to characterize the global and local fingerprint patterns. Finally, fingerprint segmentation is performed by combining the ridge density and orientation consistency for latent images. The proposed method has been evaluated on NIST SD27 latent fingerprint database. Experimental results and comparison demonstrate the promising performance of the proposed method.

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