Decoding early modern gestural patterns through hand networks

Abstract In this article, we present a new methodology and visualization system for Italian early modern painted hands. The goal is to outline potential gestural systems over a large corpus of study, in opposition to many art historical works focusing on a single hand gesture or on a specific painter. Furthermore, as most of the actual research in art history is dedicated to symbolic hands, the global approach aims to shed light on less documented hands, such as functional gestures. We first perform unsupervised clustering on a collection of hands, before combining them with classes for symbolic gestures and creating a system of recurrent hand combinations according to their corresponding iconographies. We then introduce an interactive visualization with a network graph, where the user can select an iconography and see the type of hands used in the specific narrative context. Through this experiment, we provide an analysis of the most common types of hands found in our collection, as well as the way they can be associated for particular iconographies. We finally discuss the impact of machine learning models and subjective choices induced on the results at each step, as well as implications of the study of the pictorial detail at large scale.