Rezaei, M orcid.org/0000-0003-3892-421X, Lin, J and Klette, R (2014) Hybrid filter blending to maintain facial expressions in rendered human portraits. International Journal of Arts and Technology, 7 (2-3). pp. 128-147. ISSN 1754-8853
Abstract
Artistic rendering of human portraits is different and more challenging than that of landscapes or flowers. Issues are eye, nose, and mouth regions (i.e., facial features) where we need to represent their natural emotions. Shades or darkness around eyes, or shininess at nose tips may negatively impact the rendering result if not properly dealt with. Similarly, a lighter colour around the mouth region caused by lighting might produce some disturbing artefacts. The proposed computerised method attempts to be adaptive to those sensitive areas by utilising a face analysis module. First, the program detects main facial segments and features. Then it utilises a blending of various filtering parameters aiming at an adequate final portrait that represents the subject's original facial expression, while still supporting a non-photorealistic artistic rendering as the perceived impression.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2014 Inderscience Enterprises Ltd. This item is protected by copyright. This is an author produced version of an article, published in International Journal of Arts and Technology. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | non-photorealistic rendering, NPR, artistic filter, pointillism, Glass pattern, curved-strokes style, facial features detection |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Dec 2022 11:49 |
Last Modified: | 10 Dec 2022 01:35 |
Status: | Published |
Publisher: | Inderscience |
Identification Number: | 10.1504/ijart.2014.060944 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:157584 |