2018
DOI: 10.1166/jctn.2018.7160
|View full text |Cite
|
Sign up to set email alerts
|

A Multi Resolution Transform for Thermal Face Recognition Using Random Forest Classifier

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(1 citation statement)
references
References 0 publications
0
1
0
Order By: Relevance
“…e aggregated features are then entered into the classi er. [32], K nearest neighbor(KNN) [33], Bayesian classifier(NB) [34], decision tree(DT) [35], logistic regression(LR) [36], and random forest algorithm(RF) [37]. Ensemble learning improves the effectiveness of machine learning by combining several models.…”
Section: Multilevel and Multiscale Featurementioning
confidence: 99%
“…e aggregated features are then entered into the classi er. [32], K nearest neighbor(KNN) [33], Bayesian classifier(NB) [34], decision tree(DT) [35], logistic regression(LR) [36], and random forest algorithm(RF) [37]. Ensemble learning improves the effectiveness of machine learning by combining several models.…”
Section: Multilevel and Multiscale Featurementioning
confidence: 99%