_____________________________________________________________________________________________________ *Corresponding author: E-mail: tarek@mu.edu.eg, tarek@deraya.edu.eg, tarek_1_2@yahoo.com; Asian Journal of Research in Computer Science 2(4): 1-12, 2018; Article no.AJRCOS.47298 A Robust and Efficient System to Detect Human Faces Based on Facial Features Abdelmgeid A. Ali 1 , Tarek Abd El-Hafeez 1,2* and Yosra Khalaf Mohany 1 1 Department of Computer Science, Faculty of Science, Minia University, EL-Minia, Egypt. 2 Computer Science Unit, Deraya University, EL-Minia, Egypt. Authors’ contributions This work was carried out in collaboration among all authors. Author AAA designed the study, performed the statistical analysis and wrote the protocol. Authors TAEH and YKM managed the analyses of the study, managed the literature searches and wrote the first draft of the manuscript. All authors read and approved the final manuscript. Article Information DOI: 10.9734/AJRCOS/2018/v2i430080 Editor(s): (1) Dr. Stephen Mugisha Akandwanaho, Senior Lecturer, Department of Information Systems and Technology, University of KwaZulu-Natal, South Africa. Reviewers: (1) Irena Jekova, Bulgarian Academy of Sciences, Bulgaria. (2) Zlatin Zlatev, Trakia University, Bulgaria. (3) Abdullah Sonmezoglu, Bozok, Turkey. Complete Peer review History: http://www.sdiarticle3.com/review-history/47298 Received 07 November 2018 Accepted 17 February 2019 Published 11 March 2019 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. Original Research Article