Deep Learning Based Identity Verification in Renaissance Portraits

The identity of subjects in many portraits has been a matter of debate by art historians that relied on subjective analysis of facial features. Developing automated face verification technique has thus garnered interest to provide a quantitative way to reinforce the decision arrived by the art historians. However, most existing works often fail to resolve ambiguities concerning the identity of the subjects due to significant variation in artistic styles and the limited availability and authenticity of art images. To these ends, we explore the use of deep Siamese Convolutional Neural Networks (CNN) to provide a measure of similarity between a pair of portraits. To mitigate limited training data issue, we employ CNN based style-transfer technique that creates several new images by recasting a style to other artworks, keeping original image content unchanged. The resulting system thereby learns features which are discriminative and invariant to changes in artistic styles. Our approach shows significant improvement over baselines and state-of-the-art methods on several examples which are identified by art historians as being very challenging and controversial.