Face Detection Speed Improvement using Bitmap-based Histogram of Oriented Gradients

Since the Viola-Jones seminal work, the boosted cascade with simple features has become the most popular and effective approach for practical face detection. More improved face detectors that can handle uncontrolled face detection scenarios have achieved by applying more advanced features such as Histogram of oriented Gradients (HoG). The great improvement in accuracy delivered by these methods has been accompanied by a large increase in the computational burden, which limited adoption in embedded solutions particularly. The improved bitmap-based HoG approaches resolved this problem by limitation of HoG window to non-rectangular irregular pattern of the object and its boundary avoid processing of extra background and (partially) foreground pixels respectively. In this paper, bHoG and bbHoG along with three different bitmap patterns are applied to the face detection problem to not only benefits from the robustness of HoG, but also to amend its high computational cost significantly. Experimental results show an decrease of 92.5% in the workload associated with HoG/SVM classifiers compared to the state-of-the-art, along with approximately the same performance as standard HoG and an average decrease about 5% in recall and precision in comparison for the smaller cell sizes