Quantitative modeling of artist styles in Renaissance face portraiture

Authors:
Ramya Srinivasan;Amit Roy-Chowdhury;Conrad Rudolph;Jeanette Kohl
Affiliations:
University of California, Riverside, California;University of California, Riverside, California;University of California, Riverside, California;University of California, Riverside, California
Venue:
Proceedings of the 2nd International Workshop on Historical Document Imaging and Processing
Year:
2013

Citing 11
Cited 0

Face Recognition by Elastic Bunch Graph Matching

IEEE Transactions on Pattern Analysis and Machine Intelligence
A morphable model for the synthesis of 3D faces

Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography

Communications of the ACM
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Face Processing: Advanced Modeling and Methods

Face Processing: Advanced Modeling and Methods
A review on Gabor wavelets for face recognition

Pattern Analysis & Applications
Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)

Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Computer Vision and Computer Graphics Analysis of Paintings and Drawings: An Introduction to the Literature

CAIP '09 Proceedings of the 13th International Conference on Computer Analysis of Images and Patterns
Introduction to Biometrics

Introduction to Biometrics
Rhythmic Brushstrokes Distinguish van Gogh from His Contemporaries: Findings via Automated Brushstroke Extraction

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

Renaissance portraits were depictions of some important royals of those times. Analysis of faces in these portraits can provide valuable dynastical information in addition to enriching personal details of the depicted sitter. Such studies can offer insights to the art-history community in understanding and linking personal histories. In particular, face recognition technologies can be useful for identifying subjects when there is ambiguity. However, portraits are subject to several complexities such as aesthetic sensibilities of the artist or social standing of the sitter. Thus, for robust automated face recognition, it becomes important to model the characteristics of the artist. In this paper, we focus on modeling the styles of artists by considering case studies involving Renaissance art-works. After a careful examination of artistic trends, we arrive at relevant features for analysis. From a set of instances known to match/not match, we learn distributions of match and non-match scores which we collectively refer to as the portrait feature space (PFS). Thereafter, using statistical permutation tests we learn which of the chosen features were emphasized in various works involving (a) same artist depicting same sitter, (b) same sitter but by different artists and (c) same artist but depicting different sitters. Finally, we show that the knowledge of these specific choices can provide valuable information regarding the sitter and/or artist.