New challenges of face detection in paintings based on deep learning

In this work, we address the problem of face detection from painting images in Tenebrism style, a particular painting style that is characterized by the use of extreme contrast between light and dark. We use Convolutional Neural Networks (CNNs) to tackle this task. In this article, we show that face detection in paintings presents additional challenges as compared to classic face detection from natural images. For this, we present a performance analysis of three CNN architectures, namely, VGG16, ResNet50 and ResNet101, as backbone networks of one of the most popular CNN based object detector, Faster RCNN, to boost-up the face detection performance. This paper describes a collection and annotation of benchmark dataset of Tenebrism paintings. In order to reduce the impact of dataset bias, we propose to evaluate the effect of several data augmentation techniques used to increase variability. Experimental results reveal a detection average precision of 44.19% with ResNet101, while better performances have been achieved 79.48% and 83.94% with VGG16 and ResNet50, respectively.