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Lip print-based identification using traditional and deep learning
Wardah Farrukh
,
Dustin van der Haar
Academy of Computer Science and Software Engineering
University of Johannesburg
Research output
:
Contribution to journal
›
Article
›
peer-review
10
Citations (Scopus)
Overview
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Keyphrases
Deep Learning
100%
Traditional Learning
100%
Lip Prints
100%
Individual Identity
66%
Computer Vision Methods
66%
Deep Learning Computer Vision
33%
New Alternatives
33%
Trauma
33%
Design Experiment
33%
Retina
33%
K-nearest Neighbor (K-NN)
33%
Machine Learning Classifiers
33%
Deep Learning Architectures
33%
Iris
33%
VGG19
33%
Human Lip
33%
Biometric Measures
33%
Biometric Identification
33%
Speeded up Robust Features
33%
Human Recognition
33%
Visual Geometry Group-16 (VGG16)
33%
Computer Science
Deep Learning Method
100%
Computer Vision
66%
Design Experiment
33%
Traditional Method
33%
Support Vector Machine
33%
Biometrics
33%
VGG-19 Convolutional Neural Network
33%
Biometric Identification
33%
Machine Learning
33%
Learning System
33%
VGG16
33%
Economics, Econometrics and Finance
Deep Learning Method
100%
Machine Learning
33%
Nursing and Health Professions
Finger Dermatoglyphics
100%
Social Sciences
VGG16
33%