A Comparison of Face Detection Classifier using Facial Geometry Distance Measure

Abstract

Due to the increasing crime rate in Malaysia, the safety and security need to be robust from the intruders. Numerous biometric-based technologies are offered but they are not friendly and less accurate. Among the available biometric technology, face recognition is the friendliest among all the technology. Hence, the aim of this research is to identify the best classifier for face recognition using facial geometry distance measure. A comparison between Support Vector Machine (SVM), Multi Linear Perceptron (MLP) and Naïve Bayes classifiers is conducted in classifying human face using facial geometry distance measures features. Experimental result shows Naïve Bayes obtained the high accuracy with 93.16% with less build time compared to MLP and SVM classifier. For future work, more person face images will be added into database for face recognition using the highest classifier achieves in this research.