Improving the Accuracy of Arctan for Face Detection

Abstract

Significant barriers to real time face detection have been the complexity of computation kernels, minimal costand superior accuracy requirements for both software and hardware implementation based on traditional high performance computing. It is desirable to develop variable precision face detection block for high dynamic range applications including night vision and infrared face detection applications. This paper developed an Arctan fucntion for face detection which supports input ranges upto 360 degrees for Histogram of Oriented Graph. Our implementation takes advantage of mathematical identities for the pedestrian HOG computation. We compare our HOG block design to fixed point implementations and found that using floating point HOG is not be computationally expensive and can accelerate face detection process.