A Robust and Efficient System to Detect Human Faces Based on Facial Features

Main Article Content

Abdelmgeid A. Ali
Tarek Abd El-Hafeez
Yosra Khalaf Mohany

Abstract

Face detection is considered as a one of the most important issues in the identification and authentication systems which use biometric features. Face detection is not straightforward as long as it has lots of dissimilarity of image appearance. Some challenges are required to be resolved to improve the detection process. These challenges include environmental constraints, device specific constraints and the facial feature constraints. Here in our paper we present a modified method to enrich face detection by using combination of Haar cascade files using skin detection, eye detection and nose detection. Our proposed system has been evaluated using Wild database. The experimental results have shown that the proposed system can achieve accuracy of detection up to 96%. Also, here we compared the proposed system with the other face detection systems and the success rate of the proposed system is better than the considered systems.

Keywords:
Face, skin, eye, nose detection, authentication, HSV color system, Cascade classifier, Open CV

Article Details

How to Cite
Ali, A., El-Hafeez, T., & Mohany, Y. (2019). A Robust and Efficient System to Detect Human Faces Based on Facial Features. Asian Journal of Research in Computer Science, 2(4), 1-12. https://doi.org/10.9734/ajrcos/2018/v2i430080
Section
Original Research Article