Can you really trust the sensor's PRNU? How image content might impact the finger vein sensor identification performance

2021 ◽  
Author(s):  
Dominik Sollinger ◽  
Luca Debiasi ◽  
Andreas Uhl
Keyword(s):  
Identification Performance ◽  
Image Content ◽  
Finger Vein

Individual Differences Predict Eyewitness Identification Performance

2012 ◽  
Author(s):  
Shannon M. Andersen ◽  
Curt A. Carlson ◽  
Maria Carlson ◽  
Scott D. Gronlund
Keyword(s):  
Individual Differences ◽  
Eyewitness Identification ◽  
Identification Performance

IRIS AND FINGER VEIN MULTI MODEL RECOGNITION SYSTEM BASED ON SIFT FEATURES

2018 ◽  
Vol 1 (2) ◽  
pp. 34-44
Author(s):  
Faris E Mohammed ◽  
Dr. Eman M ALdaidamony ◽  
Prof. A. M Raid
Keyword(s):  
Iris Recognition ◽  
Recognition Performance ◽  
Recognition System ◽  
Individual Identification ◽  
Work Place ◽  
Identification Process ◽  
Finger Vein ◽  
Noise Point ◽  
Vein Recognition ◽  
A New Technique

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems.   © 2018 JASET, International Scholars and Researchers Association    

A Novel Verification Scheme for Watermarking Based Image Content Authentication Systems

2008 ◽  
Vol 67 (19) ◽  
pp. 1777-1790 ◽  
Author(s):  
C. Cruz-Ramos ◽  
R. Reyes-Reyes ◽  
J. Mendoza-Noriega ◽  
Mariko Nakano-Miyatake ◽  
Hector Manuel Perez-Meana
Keyword(s):  
Content Authentication ◽  
Image Content ◽  
Verification Scheme

Improving person re-identification performance by customized dataset and person detection

2019 ◽  
Vol 2019 (11) ◽  
pp. 268-1-268-9
Author(s):  
Herman G.J Groot ◽  
Egor Bondarev ◽  
Peter H.N. de With
Keyword(s):  
Identification Performance ◽  
Person Detection

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

Sistem Pengawasan Aktifitas Penggunaan Smartphone Android

2018 ◽  
Vol 9 (1) ◽  
pp. 24-31
Author(s):  
Rudianto Rudianto ◽  
Eko Budi Setiawan
Keyword(s):  
Application Programming Interface ◽  
Third Party ◽  
Image Content ◽  
Image File ◽  
Application Programming ◽  
Index Terms ◽  
Programming Interface

Availability the Application Programming Interface (API) for third-party applications on Android devices provides an opportunity to monitor Android devices with each other. This is used to create an application that can facilitate parents in child supervision through Android devices owned. In this study, some features added to the classification of image content on Android devices related to negative content. In this case, researchers using Clarifai API. The result of this research is to produce a system which has feature, give a report of image file contained in target smartphone and can do deletion on the image file, receive browser history report and can directly visit in the application, receive a report of child location and can be directly contacted via this application. This application works well on the Android Lollipop (API Level 22). Index Terms— Application Programming Interface(API), Monitoring, Negative Content, Children, Parent.

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