Generic Face Alignment Using an Improved Active Shape Model
Although conventional Active Shape Model (ASM) and Active Appearance Model (AAM) based approaches have achieved some success, however, evidence suggests that the performance of a person-specific face alignment which aligns the variation in appearance of a single person across pose, illumination, and expression is substantially better than the performance of generic face alignment which aligns the variation in appearance of many faces, including unseen faces not in the training set. This paper proposes a discriminative framework for generic face alignment. This technique is presented under the framework of conventional Active Shape Model (ASM) but has three improvements. First, random forest classifiers are trained to recognize local appearance around each landmark. This discriminative learning provides more robustness weight for the optimization fitting procedure. Second, to impose constrains, shape vectors are restricted to the vector space spanned by the training database. Third, data augment scheme is used for the benefit of a large training set. Experimental results show that this approach can achieve good performance on generic face alignment.
Liting Wang Xiaoqing Ding Chi Fang
Electronic Engineering Department, Tsinghua University, Beijing, China
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
317-321
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）