Improvements on Search Process and Search Subspace in Active Shape Model
HE Liang-Hua1, HU Die2, JIANG Chang-Jun1
1.School of Electronics and Information Engineering, Tongji University, Shanghai 2000922. Department of Communication Science and Engineering, Fudan University, Shanghai 200433
Abstract：The method of active shape model (ASM) is quite commonly used for alignment in recent years. However, its search subspace has a number of limitations on reconstructing the changeable shape in real life. In addition, since there is no restriction during searching, the result is unstable. In this paper, some improvements on search subspace and search process are proposed. By adding eigen-shape variance information to search subspace, the new search subspace can reconstruct the shape more generally. In the meantime, the search errors are analyzed and searching information of the next step is obtained. The whole search processing is iterated and connected with the feedback of search error. The feedback and iteration make the search processing more active and the final search result is unique. Several face alignment experiments are designed and the results show that the proposed method improves alignment precision greatly.
何良华，胡蝶，蒋昌俊. 主动形状模型中搜索过程与搜索空间的改进*[J]. 模式识别与人工智能, 2008, 21(3): 394-400.
HE Liang-Hua, HU Die, JIANG Chang-Jun. Improvements on Search Process and Search Subspace in Active Shape Model. , 2008, 21(3): 394-400.
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2008, Vol. 21 
