Face Recognition Using the Direct GLCM and K-NN Methods

Abstract

Abstract—This paper introduces a new face recognition method based on the gray-level co-occurrence matrix (GLCM). This method directly uses GLCM by converting a matrix into a vector that can be used as a feature vector for the classiï¬cation process, this method is called direct GLCM. The classiï¬cation process used is K-Nearest Neighbor (K-NN), in which the classiï¬cation process compares the features contained in K-NN namely Euclidean distance, Cityblock, Chebychev, and Minkowski. The results show that using direct GLCM as a feature vector in the recognition process using the K-NN classiï¬cation with the Cityblock feature produces an accuracy of 84.29%, FAR 6.67% and FRR 9.05%. Index Terms—Face recognition, Gray-Level Co-occurrence Matrix, K-Nearest Neighbor, Cityblock