基于人脸相似度加权距离的非特定人表情识别

谭华春; 章毓晋

doi:10.3724/SP.J.1146.2005.00790

基于人脸相似度加权距离的非特定人表情识别

doi: 10.3724/SP.J.1146.2005.00790

基金项目:

教育部高等学校博士学科点专项科研基金(RFDP-20020003011)资助课题

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出版历程
- 收稿日期: 2005-07-07
- 修回日期: 2006-06-06
- 刊出日期: 2007-02-19

Person-Independent Facial Expression Recognition Based on Person-Similarity Weighted Distance

摘要

摘要: 该文提出了一种用于非特定人表情识别的方法。首先，对测试人的初始表情特征进行高阶奇异值分解，得到测试人与训练集中所有人相关的表情特征。然后，根据相似的人有相似的表情的假设，计算人脸相似度加权距离，作为测试人的表情特征与标准的表情特征之间的相似性测度。通过加权的过程，可以有效地去除由于个体差异而造成的表情特征的差异，提高非特定人表情识别的鲁棒性。该文提出的方法在JAFFE数据库上进行了测试。对非特定人的表情识别实验表明，该文方法比传统的方法在识别率上有了提高。
- 人脸表情识别;非特定人;人脸相似度加权距离;高阶奇异值分解
Abstract: In this paper, a new distance called person-similarity weighted distance is proposed for person-independent facial expression recognition. In the new method, expression features associated with all persons in training set are extracted by Higher-Order Singular Value Decomposition (HOSVD) firstly. Then, based on the assumption similar persons have similar facial expression, the person-similarity weighted distance is calculated to measure the similarity between the test expression and standard expressions. By the weighting process, the distance can remove the differences caused by individual and becomes less person-dependent. Experimental results show the superiority of proposed method over the existing methods.

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参考文献(1)

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