Face Detection in Painting Using Deep Convolutional Neural Networks

The artistic style of paintings constitutes an important information about the painter’s technique. It can provide a rich description of this technique using image processing tools, and particularly using image features. In this paper, we investigate automatic face detection in the Tenebrism style, a particular painting style that is characterized by the use of extreme contrast between the light and dark. We show that convolutional neural network along with an adapted learning base makes it possible to detect faces with a maximum accuracy in this style. This result is particularly interesting since it can be the basis of an illuminant study in the Tenebrism style.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Li-Jia Li,et al.  Multi-view Face Detection Using Deep Convolutional Neural Networks , 2015, ICMR.

[3]  Zhengyou Zhang,et al.  A Survey of Recent Advances in Face Detection , 2010 .

[4]  Stefanos Zafeiriou,et al.  A survey on face detection in the wild: Past, present and future , 2015, Comput. Vis. Image Underst..

[5]  Harry Wechsler,et al.  Modern art challenges face detection , 2019, Pattern Recognit. Lett..

[6]  Florian Yger,et al.  Recognizing Art Style Automatically in Painting with Deep Learning , 2017, ACML.

[7]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[8]  David G. Stork,et al.  Computer Vision, Image Analysis, and Master Art: Part 2 , 2006, IEEE MultiMedia.

[9]  Gang Hua,et al.  A convolutional neural network cascade for face detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).