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Random Forest and SVM Based Face Recognition Using Subspace Methods

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DOI: 10.23977/isspj.2018.31002 | Downloads: 128 | Views: 280

Author(s)

Amir Amini 1, Ali Pourmohammad 2, Ali Asghar Khosravi 1

Affiliation(s)

1 Department of Electrical Engineering, Faculty of Engineering and Technology, West Tehran Branch – Islamic Azad University, Tehran, IRAN
2 Electrical Engineering Department, Amirkabir University of Technology, Tehran, IRAN

Corresponding Author

Ali Pourmohammad

ABSTRACT

Face recognition is one of the methods used to identifying the people. Due to its ease of use, this method has been used in recent decades. This method is more user friendly than other people identifying methods through iris, retina, fingerprint, etc. Because of the lack of cooperation of the person being examined, the face recognition method is more acceptable than others. In this paper, using one of the Subspace Methods as the face attribute extractor and applying its pre-processing technique as the initial stage of the face recognition system, has been investigated for increasing the face recognition rate, and extraction of a certain aspect of the face has led to improvement in the correctness of the diagnosis. The subspace algorithm, by highlighting the features needs to identify and remove unnecessary information, reduces the volume of computations and increases the speed of detection. Then 80% of the data is trained by support vector machine and random forests, both of which are classifiers, and tested on 20% of the data. In this paper, due to changes in its pre-processing and changes in the way of extracting the characteristics, the results are obtained efficient.

KEYWORDS

Support Vector Machine, Random Forest, Subspace Methods, PCA, LDA

CITE THIS PAPER

Ali Asghar, Khosravi, Ali Pourmohammad and Amir Amini, Random Forest and SVM Based Face Recognition Using Subspace Methods, Information Systems and Signal Processing Journal (2018) Vol. 3: 5-13.

REFERENCES

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[2] Kremic, Emir, and Abdulhamit Subasi. "Performance of Random Forest and SVM in Face Recognition." The International Arab Journal of Information Technology (2015).
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[4] Shanshan Guo, Shiyu Chen, Yanjie Li, “ Face Recignition Based on Convolutional Neural Network and Support Vector Machine “, Proceedings of the IEEE, Ningbo, China, August 2016.
[5] Shih-Hsuan Yang, Yu-Xiang Zhan, “Improved Face Recognition by Incorporating Local Color Information into the Active Shape Model”. Machine Learning and Cybernetics, Jeju, South Korea, 10-13 July, 2016
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