We present a work that explores the feasibility of automated face recognition technologies for analyzing identities in works of portraiture, and in the process provide additional evidence to settle some long-standing questions in art history. 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. From a set of portraiture of the Renaissance and Baroque periods, where the identities of subjects are known, we derive appropriate features that are based on domain knowledge of artistic renderings, and learn and validate statistical models for the distribution of the match and non-match scores, which we refer to as portrait feature space (PFS). Thereafter, we use this PFS on a number of cases that have been "open questions" to art historians. They are usually in the form of validating two portraits as belonging to the same person. Using statistical hypothesis tests on the PFS, we provide quantitative measures of similarity for each of these questions. It is, to the best of our knowledge, the first study that applies automated face recognition technologies to the analysis of portraits of multiple subjects in various forms - paintings, death masks, sculptures.