Comparison of Geometric Features and Color Features for Face Recognition

Abstract

Face recognition plays an important role in the identity recognition system, the color and geometry feature has been claimed able to be used as parameter for face recognition. This study aims to analize the performance of geometric features, color features, and both of them on the human face using Gaussian Naïve Bayes (GNB) and the other Machine Learning method. This study using various geometric features: the distance between the eyes, nose, mouth by using Euclidean distance, and classified using GNB, K-Nearest Neighbour (KNN), and Support Vector Machine (SVM). The result compared with color feature: normalized RGB values, mean of normalized RGB, and RGB Variant as color features. The feature values obtained are assembled and processed using GNB and the other ML method to classified and recognized the faces. The dataset obtained from Aberdeen faces the dataset, which has 687 color faces from Ian Craw at Aberdeen. Between 1 and 18 images of 90 individuals. Some variations in lighting, varied viewpoints, and the resolution have varied between 336x480 to 624x544. The experimental results show that the system successfully recognized the face based on the determined algorithm and based on three models, SVM reached nearly 74.83%, GNB reached nearly 74.67%, and KNN with K = 5 reached nearly 72.17%.