  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. In this paper, we study
the influence of a set of blind pre-processing 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 pre-processing 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 pre-processing method that may hurt the
face detector. The second set is performed to study the effect of the selected
pre-processing methods on images that suffer from hard conditions. We present
two manners of applying the pre-processing method to the image prior to being
used by the Viola-Jones face detector. Four 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 pre-processing
methods may hurt the accuracy of Viola-Jones face detection algorithm. However,
other pre-processing 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 pre-processing step in order to improve
the accuracy of the pre-trained Viola-Jones face detector.
