2019
DOI: 10.1080/02522667.2019.1582875
|View full text |Cite
|
Sign up to set email alerts
|

A review of face recognition methods using deep learning network

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
5
0

Year Published

2019
2019
2023
2023

Publication Types

Select...
9
1

Relationship

0
10

Authors

Journals

citations
Cited by 24 publications
(5 citation statements)
references
References 15 publications
0
5
0
Order By: Relevance
“…Face detection is to extract the salient features of the face image, and face recognition is to extract the features of the face; the existing features are compared to determine which kind of face the feature belongs to, and the two have essential differences. [16,17].…”
Section: Related Workmentioning
confidence: 99%
“…Face detection is to extract the salient features of the face image, and face recognition is to extract the features of the face; the existing features are compared to determine which kind of face the feature belongs to, and the two have essential differences. [16,17].…”
Section: Related Workmentioning
confidence: 99%
“…They done a comprehensive review about the challenges, applications of face detection system, feature based and image based FD approaches, Statistical based FD system and the databases used for FD and concluded with the future directions for research on FD. Arunkumar&jain [48] reviewed and compared the FD techniques that are applicable for face detection from images and videos. They also listed the Face recognition models that are frequently used and the databases that are publically available for face recognition.…”
Section: Review About the Face Detection Approaches Using Ml/ Deep Learning Algorithmsmentioning
confidence: 99%
“…Deep learning has become the predominant solution for most facial analysis problems. The preference for deep learning models in the computer vision field is due to the robustness of architectures such as convolutional neural networks or residual connections, which effectively extracts all the necessary facial features without manually engineering the features using several filters [1,2]. Gender estimation is the idea of training a machine learning model on thousands of labelled facial images to produce a function that maps an input image X to a corresponding label Y.…”
Section: Introductionmentioning
confidence: 99%