Identifying Ethnics of People through Face Recognition: A Deep CNN Approach
Joint Authors
AlBdairi, Ahmed Jawad A.
Xiao, Zhu
Alghaili, Mohammed
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-7, 7 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-14
Country of Publication
Egypt
No. of Pages
7
Main Subjects
Abstract EN
The interest in face recognition studies has grown rapidly in the last decade.
One of the most important problems in face recognition is the identification of ethnics of people.
In this study, a new deep learning convolutional neural network is designed to create a new model that can recognize the ethnics of people through their facial features.
The new dataset for ethnics of people consists of 3141 images collected from three different nationalities.
To the best of our knowledge, this is the first image dataset collected for the ethnics of people and that dataset will be available for the research community.
The new model was compared with two state-of-the-art models, VGG and Inception V3, and the validation accuracy was calculated for each convolutional neural network.
The generated models have been tested through several images of people, and the results show that the best performance was achieved by our model with a verification accuracy of 96.9%.
American Psychological Association (APA)
AlBdairi, Ahmed Jawad A.& Xiao, Zhu& Alghaili, Mohammed. 2020. Identifying Ethnics of People through Face Recognition: A Deep CNN Approach. Scientific Programming،Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1209062
Modern Language Association (MLA)
AlBdairi, Ahmed Jawad A.…[et al.]. Identifying Ethnics of People through Face Recognition: A Deep CNN Approach. Scientific Programming No. 2020 (2020), pp.1-7.
https://search.emarefa.net/detail/BIM-1209062
American Medical Association (AMA)
AlBdairi, Ahmed Jawad A.& Xiao, Zhu& Alghaili, Mohammed. Identifying Ethnics of People through Face Recognition: A Deep CNN Approach. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-7.
https://search.emarefa.net/detail/BIM-1209062
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209062