Partially Covered Face Detection in Presence of Headscarf for Surveillance Applications

Abstract

In the past few years, the application of surveillance for security and smart cities are growing rapidly. The human detection based on the surveillance videos is a complex task and traditional clothing such as headscarf makes this task even more difficult. The surveillance systems designed for many countries are required to be able to recognize the people with these traditional clothing. In this paper, a computer vision system for partially covered face detection in low resolution surveillance videos containing traditional Middle Eastern clothing including the headscarf is presented. The proposed framework uses a combination of Haar cascade and Locally Binary Patterns Histogram (LBPH) for feature extraction and the Support Vector Machine (SVM) algorithm for face classification. A large dataset of a crowded office environment in Middle East is collected and used for evaluation of the proposed model. The experimental results show that the proposed method has acceptable results for face detection in complex surveillance scenarios.