Detection face parts in image using neural network based on MATLAB
Joint Authors
Ghalib, Shahd L.
Abd al-Rahman, Asma A.
Tahir, Fuad Sh.
Source
Engineering and Technology Journal
Issue
Vol. 39, Issue 1B (31 Jan. 2021), pp.159-164, 6 p.
Publisher
Publication Date
2021-01-31
Country of Publication
Iraq
No. of Pages
6
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Recently, face recognition system (FRS) is implemented in different applications including a range of vital services like airports and banking systems for security purposes.
Therefore, deployed surveillance systems have been established which led to the urgent need to develop a vital face recognition system.
In this work, a new algorithm was proposed for recognition of the face, personal and color images by training the convolutional neural network using the MATLAB program to build a new program for detection of the face, then building a separate program to discover the lips, nose, and eyes, New methods were explored to analyze the main and independent components to improve face detection, which is considered one of the important techniques in this work using neural networks and implementation through the MATLAB program.
American Psychological Association (APA)
Ghalib, Shahd L.& Tahir, Fuad Sh.& Abd al-Rahman, Asma A.. 2021. Detection face parts in image using neural network based on MATLAB. Engineering and Technology Journal،Vol. 39, no. 1B, pp.159-164.
https://search.emarefa.net/detail/BIM-1282624
Modern Language Association (MLA)
Ghalib, Shahd L.…[et al.]. Detection face parts in image using neural network based on MATLAB. Engineering and Technology Journal Vol. 39, no. 1B (2021), pp.159-164.
https://search.emarefa.net/detail/BIM-1282624
American Medical Association (AMA)
Ghalib, Shahd L.& Tahir, Fuad Sh.& Abd al-Rahman, Asma A.. Detection face parts in image using neural network based on MATLAB. Engineering and Technology Journal. 2021. Vol. 39, no. 1B, pp.159-164.
https://search.emarefa.net/detail/BIM-1282624
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 164
Record ID
BIM-1282624