Learning to Sketch Human Facial Portraits using Personal Styles by Case-Based Reasoning

TL;DR

This paper introduces a case-based reasoning approach to generate personalized facial portraits by mimicking individual artists' styles through iterative style transfer and case adaptation.

Contribution

It presents a novel CBR framework with learned models for style evaluation and parameter estimation, enabling personalized and iterative facial sketch synthesis.

Findings

Outperforms state-of-the-art facial sketch algorithms in style accuracy

Effectively captures individual artists' unique illustration styles

Demonstrates superior aesthetic quality in generated sketches

Abstract

This paper employs case-based reasoning (CBR) to capture the personal styles of individual artists and generate the human facial portraits from photos accordingly. For each human artist to be mimicked, a series of cases are firstly built-up from her/his exemplars of source facial photo and hand-drawn sketch, and then its stylization for facial photo is transformed as a style-transferring process of iterative refinement by looking-for and applying best-fit cases in a sense of style optimization. Two models, fitness evaluation model and parameter estimation model, are learned for case retrieval and adaptation respectively from these cases. The fitness evaluation model is to decide which case is best-fitted to the sketching of current interest, and the parameter estimation model is to automate case adaptation. The resultant sketch is synthesized progressively with an iterative loop of retrieval and adaptation of candidate cases until the desired aesthetic style is achieved. To explore the effectiveness and advantages of the novel approach, we experimentally compare the sketch portraits generated by the proposed method with that of a state-of-the-art example-based facial sketch generation algorithm as well as a couple commercial software packages. The comparisons reveal that our CBR based synthesis method for facial portraits is superior both in capturing and reproducing artists' personal illustration styles to the peer methods.