A comparison of face detection classifier using facial geometry distance measure

• Published in: 2018 9th IEEE Control and System Graduate Research Colloquium, ICSGRC 2018 - Proceeding
• Main Author: Sabri N.; Henry J.; Ibrahim Z.; Ghazali N.; Abu Mangshor N.N.; Mohd Johari N.F.; Ibrahim S.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2018
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85063527781&doi=10.1109%2fICSGRC.2018.8657592&partnerID=40&md5=68a0579f7e62a7ba51b834d16eb3043a

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. © 2018 IEEE.