Random Forest and SVM Based Face Recognition Using Subspace Methods
Ali Asghar Khosravi, Amir Shahrokh Amini, Ali Pourmohammad · Information Systems and Signal Processing Journal · 2018
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.