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Performance of random forest and SVM in face recognition
Joint Authors
Kremic, Amir
Subasi, Abd al-Hamid
Source
The International Arab Journal of Information Technology
Issue
Vol. 13, Issue 2 (31 Mar. 2016)7 p.
Publisher
Publication Date
2016-03-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
In the study We Ptesnent the performance of Random Forest (RF) and Support Vector Machine (SVM) in facial recognition.
Random Forest Tree (RFT) based algorithm is popular in computer vision and in solving the facial recognition.
SVM is a machine learning method and has been used for classification of face recognition.
The kernel parameters were used for optimization.
The testing has been comportment from the International Burch University (IBU) image databases.
Each person consists of 20 single individual photos, with different facial expression and size 205 × 274 px.
The SVM achieved accuracy of 93.
20 %, but when optimized with different classifiers and kernel accuracy among all was 95.
89 %, 96.
92 %, 97.
94 %.
RF achieved accuracy of 97.
17 %.
The approach was as follow: Reads image, skin color detection, RGB to gray, histogram, performance of SVM, RF and classification.
All research and testing which were conducted are with aim to be integrated in mobile application for face detection, where application can perform with higher accuracy and performance.
American Psychological Association (APA)
Kremic, Amir& Subasi, Abd al-Hamid. 2016. Performance of random forest and SVM in face recognition. The International Arab Journal of Information Technology،Vol. 13, no. 2.
https://search.emarefa.net/detail/BIM-581166
Modern Language Association (MLA)
Kremic, Amir& Subasi, Abd al-Hamid. Performance of random forest and SVM in face recognition. The International Arab Journal of Information Technology Vol. 13, no. 2 (Mar. 2016).
https://search.emarefa.net/detail/BIM-581166
American Medical Association (AMA)
Kremic, Amir& Subasi, Abd al-Hamid. Performance of random forest and SVM in face recognition. The International Arab Journal of Information Technology. 2016. Vol. 13, no. 2.
https://search.emarefa.net/detail/BIM-581166
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-581166