Face Recognition Using Random Forest Classifiers Based on PCA, LDA and LBP Features

Face is the main part of human beings to distinguish from one another. Face recognition system mainly takes an image as an input and compares this image with a number of images stored in the database to identify whether the input image is in the database or not. Also, face recognition is the process of identification and verification of individuals by their facial images. In this thesis, well-known databases such as FERET and JAFFE databases are used for experimental evaluations. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Patterns (LBP) are used for extracting facial features of individuals from the region of interests. Decision Tree (DT) and Random Forest (RF) are used as classify the faces based on extracted features. The Manhattan Distance measure is used to compare the difference between test and training images for face recognition. Based on the experimental evaluations, the achieved recognition rates are very close to those published articles in the literature. Keywords: Local Binary Patterns (LBP), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Random Forest (RF), Decision Tree (DT), feature extraction, classification.