Can We Boost the Power of the Viola-Jones Face Detector Using Pre-processing? An Empirical Study

TL;DR

This study investigates how various blind pre-processing techniques affect the Viola-Jones face detector's performance, finding that some methods improve accuracy while others may hinder it, leading to recommendations for effective pre-processing steps.

Contribution

The paper provides an empirical analysis of pre-processing methods on Viola-Jones face detection, identifying effective techniques to enhance detection accuracy under challenging conditions.

Findings

Certain pre-processing methods improve detection accuracy.

Some pre-processing techniques can negatively impact performance.

Recommended three simple pre-processing methods for better results.

Abstract

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.