教師資料查詢 | 類別: 期刊論文 | 教師: 珽 CHING-TING TU (瀏覽個人網頁)

標題:Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network
學年105
學期1
出版(發表)日期2016/08/01
作品名稱Facial Sketch Synthesis Using 2D Direct Combined Model-Based Face-Specific Markov Network
作品名稱(其他語言)
著者Ching-Ting Tu; Yu-Hsien Chan; Yi-Chung Chen
單位
出版者
著錄名稱、卷期、頁數IEEE Transactions on Image Processing 25(8), pp.3546-3561
摘要A facial sketch synthesis system is proposed, featuring a 2D direct combined model (2DDCM)-based face-specific Markov network. In contrast to the existing facial sketch synthesis systems, the proposed scheme aims to synthesize sketches, which reproduce the unique drawing style of a particular artist, where this drawing style is learned from a data set consisting of a large number of image/sketch pairwise training samples. The synthesis system comprises three modules, namely, a global module, a local module, and an enhancement module. The global module applies a 2DDCM approach to synthesize the global facial geometry and texture of the input image. The detailed texture is then added to the synthesized sketch in a local patch-based manner using a parametric 2DDCM model and a non-parametric Markov random field (MRF) network. Notably, the MRF approach gives the synthesized results an appearance more consistent with the drawing style of the training samples, while the 2DDCM approach enables the synthesis of outcomes with a more derivative style. As a result, the similarity between the synthesized sketches and the input images is greatly improved. Finally, a post-processing operation is performed to enhance the shadowed regions of the synthesized image by adding strong lines or curves to emphasize the lighting conditions. The experimental results confirm that the synthesized facial images are in good qualitative and quantitative agreement with the input images as well as the ground-truth sketches provided by the same artist. The representing power of the proposed framework is demonstrated by synthesizing facial sketches from input images with a wide variety of facial poses, lighting conditions, and races even when such images are not included in the training data set. Moreover, the practical applicability of the proposed framework is demonstrated by means of automatic facial recognition tests.
關鍵字Direct Combined Model (DCM);Canonical Correlation Analysis (CCA);Markov Random Field (MRF);Face Sketch Synthesis, Statistical Image Models
語言英文
ISSN
期刊性質國內
收錄於SCI;
產學合作
通訊作者
審稿制度
國別中華民國
公開徵稿
出版型式,電子版
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