Can we boost the power of the Viola–Jones face detector using preprocessing? An empirical study
Mahmoud Afifi, Marwa Nasser, Mostafa Korashy, Katherine Rohde, Aly A. Mohamed · Journal of Electronic Imaging · 2018
The Viola–Jones face detection algorithm was (and still is) a quite popular face detector. In spite of the numerous face detection techniques that have been recently presented, there are many research works that are still based on the Viola–Jones algorithm because of its simplicity. We study the influence of a set of blind preprocessing methods on the face detection rate using the Viola–Jones algorithm. We focus on two aspects of improvement, specifically badly illuminated faces and blurred faces. Many methods for lighting invariant and deblurring are used in order to improve the detection accuracy. We want to avoid using blind preprocessing methods that may obstruct the face detector. To that end, we perform two sets of experiments. The first set is performed to avoid any blind preprocessing method that may hurt the face detector. The second set is performed to study the effect of the selected preprocessing methods on images that suffer from hard conditions. We present two manners of applying the preprocessing method to the image prior to being used by the Viola–Jones face detector. Five different datasets are used to draw a coherent conclusion about the potential improvement caused by using prior enhanced images. The results demonstrate that some of the preprocessing methods may hurt the accuracy of the Viola–Jones face detection algorithm. However, other preprocessing methods have an evident positive impact on the accuracy of the face detector. Overall, we recommend three simple and fast blind photometric normalization methods as a preprocessing step in order to improve the accuracy of the pretrained Viola–Jones face detector.