UC Riverside

© 2015 IEEE. This article explores the feasibility of face-recognition technologies for analyzing works of portraiture and, in the process, provides a quantitative source of evidence to art historians in answering many of their ambiguities concerning identity of the subject in some portraits and in understanding artists? styles. Works of portrait art bear the mark of visual interpretation of the artist. Moreover, the number of samples available to model these effects is often limited. Based on an understanding of artistic conventions, we show how to learn and validate features that are robust in distinguishing subjects in portraits (sitters) and that are also capable of characterizing an individual artist?s style. This can be used to learn a feature space called portrait feature space (PFS) that is representative of quantitative measures of similarities between portrait pairs known to represent same/different sitters. Through statistical hypothesis tests, we analyze uncertain portraits against known identities and explain the significance of the results from an art historian?s perspective. Results are shown on our data consisting of over 270 portraits belonging largely to the Renaissance era.