Victim Identification Center

2011 ◽  
pp. 227-294
Keyword(s):  
Victim Identification

The contribution of DNA to the disaster victim identification (DVI) effort

2011 ◽  
Vol 205 (1-3) ◽  
pp. 52-58 ◽  
Author(s):  
D. Hartman ◽  
O. Drummer ◽  
C. Eckhoff ◽  
J.W. Scheffer ◽  
P. Stringer
Keyword(s):  
Disaster Victim Identification ◽  
Victim Identification ◽  
Disaster Victim

scholarly journals Disaster Victim Identification and ICT

2015 ◽  
Vol 9 (2) ◽  
pp. 119-130
Author(s):  
Takafumi AOKI ◽  
Koichi ITO ◽  
Shoichiro AOYAMA
Keyword(s):  
Disaster Victim Identification ◽  
Victim Identification ◽  
Disaster Victim

scholarly journals Palmprint Recognition in Uncontrolled and Unco-operative Environment

2021 ◽  
Vol 9 (8) ◽  
pp. 1711-1718
Author(s):  
Jayakrishnan S Kumar
Keyword(s):  
Deep Learning ◽  
Learning Rule ◽  
Industrial Applications ◽  
Palmprint Recognition ◽  
User Cooperation ◽  
Victim Identification ◽  
Palmprint Identification ◽  
On Line ◽  
End To End ◽  
Area Unit

Abstract: On-line palmprint recognition and latent palmprint identification unit two branches of palmprint studies. The previous uses middle-resolution footage collected by a camera in an exceedingly} very well-controlled or contact-based surroundings with user cooperation for industrial applications and so the latter uses high resolution latent palmprints collected in crime scenes for rhetorical investigation. However, these two branches do not cowl some palmprint footage that have the potential for rhetorical investigation. Attributable to the prevalence of smartphone and shopper camera, further proof is at intervals the variability of digital footage taken in uncontrolled and uncooperative surroundings. However, their palms area unit typically noticeable. To visualize palmprint identification on footage collected in uncontrolled and uncooperative surroundings, a novel palmprint info is established Associate in nursing AN end-to-end deep learning rule is projected. The new data named NTU Palmprints from the net (NTU-PI-v1) contains 7881 footage from 2035 palms collected from the net. The projected rule consists of Associate in Nursing alignment network and a feature extraction network and is end-to-end trainable. The projected rule is compared with the progressive on-line palmprint recognition ways that and evaluated on three public contactless palmprint infos, IITD, CASIA, and PolyU and a couple of new databases, NTU-PI-v1 and NTU contactless palmprint info. The experimental results showed that the projected rule outperforms the current palmprint recognition ways that. Keywords: Biometrics, criminal and victim identification, forensics, palmprint recognition

scholarly journals Forensic odontology as a victim identification tool in mass disasters: A feasibility study in the Indian scenario

2014 ◽  
Vol 6 (1) ◽  
pp. 58 ◽  
Author(s):  
Shamindra Sengupta ◽  
Vandana Sharma ◽  
Hitesh Vij ◽  
Ruchieka Vij ◽  
Kanika Prabhat ◽  
...  
Keyword(s):  
Feasibility Study ◽  
Forensic Odontology ◽  
Victim Identification ◽  
Mass Disasters ◽  
Indian Scenario ◽  
Identification Tool

scholarly journals Approaching disaster victim identification

2014 ◽  
Vol 33 (3) ◽  
pp. 233-238 ◽  
Author(s):  
Jackie Leach Scully ◽  
Robin Williams
Keyword(s):  
Disaster Victim Identification ◽  
Victim Identification ◽  
Disaster Victim
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