  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.
