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Machine Learning With Limited Data: Application in Art Portraits

Creative Commons 'BY-NC-SA' version 4.0 license

The identity of subjects in many portraits has been a matter of debate for art historians that relied upon a subjective analysis of facial features to resolve ambiguity in sitter identity. Developing automated face verification techniques has thus garnered interest to provide a quantitative way to reinforce the decision arrived at by 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 the limited training data issues, we employ CNN based style-transfer technique that creates several new images by recasting an art style to other images, keeping original image content unchanged. The resulting system thereby learns features that 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.

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