Face Detection Using Web Images

The current facial recognition algorithms struggle with accuracy on real world cases. Haar cascade based algorithms are fast, but require fine tuning per image in order to achieve the best results. When tasked with images where there are multiple faces at different locations, the current algorithms seem to underreport the number of faces. This study attempts to produce a more accurate classifier through the use of taking the maximum result of multiple Haar cascade classifiers with differing parameters. To do this, a web image scraper was written to gather real world images from Google images and Flickr. These images were analyzed using the OpenCV library utilizing multiple Haar cascade classifiers and the maximum of these classifiers was taken. The result is a more accurate classifier, as most of the inaccuracies were due to undercounting, rather than overcounting