Where’s Wally: A Gigapixel Image Study for Face Recognition in Crowds

Several devices are capable of capturing images with a large number of people, including those of high resolution known as gigapixel images. These images can be helpful for studies and investigations, such as finding people in a crowd. Although they can provide more details, the task of identifying someone in the crowd is quite challenging and complex. In this paper, we aim to assist the work of a human observer with larger images with crowds by reducing the search space for several images to a ranking of ten images related to a specific person. Our model collects faces in a crowded gigapixel image and then searches for people using three different poses (front, right and left). We built a handcraft dataset with 42 people to evaluate our method, achieving a recognition rate of 69% in the complete dataset. We highlight that, from the 31% “not found” among the top ten in the ranking, many of them are very close to this boundary and, in addition, 92% of non-matched are occluded by some accessory or another face. Experimental results showed great potential for our method to support a human observer to find people in the crowd, especially cluttered images, providing her/him with a reduced search space.