Face model fitting on video sequences based on incremental virtual active appearance model

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• 作者：Ying Chen ; Chunjian Hua ; Xiuxiao Guo
• 关键词：Active appearance model ; Virtual subspace ; Incremental learning ; Poison fusion
• 刊名：Multidimensional Systems and Signal Processing
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：28
• 期：1
• 页码：1-21
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Circuits and Systems; Electrical Engineering; Signal,Image and Speech Processing; Artificial Intelligence (incl. Robotics);
• 出版者：Springer US
• ISSN：1573-0824
• 卷排序：28

文摘

It has been proved that the performance of a person-specific active appearance model (AAM) built to model the appearance variation of a single person across pose, illumination, and expression is substantially better than the performance of a generic AAM built to model the appearance variation of many faces. However, it is not practical to build a personal AAM before tracking an unseen subject. A virtual person-specific AAM is proposed to tackle the problem. The AAM is constructed from a set of virtual personal images with different poses and expressions which are synthesized from the annotated first frame via regressions. To preserve personal facial details on the virtual images, a poison fusion strategy is designed and applied to the virtual facial images generated via bilinear kernel ridge regression. Furthermore, the AAM subspace is sequentially updated during tracking based on sequential Karhunen–Loeve algorithm, which helps the AAM adaptive to the facial context variation. Experiments show the proposed virtual personal AAM is robust to facial context changes during tracking, and outperforms other state-of-the-art AAM on facial feature tracking accuracy and computation cost.