Performance of Random Forest and SVM in Face Recognition

In this study, we present 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 205×274PX. 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 is with aim to be integrated in mobile application for face detection, where application can perform with higher accuracy and performance.