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首页> 外文会议>International Symposium on Computer and Information Sciences(ISCIS 2006); 20061101-03; Istanbul(TR) >Design of a Feature Set for Face Recognition Problem
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Design of a Feature Set for Face Recognition Problem

机译：人脸识别问题的特征集设计

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摘要

An important problem in face recognition is the design of the feature space which represents the human face. Various feature sets have been and are continually being proposed for this purpose. However, there exists no feature set which gives a superior and consistent recognition performance on various face databases. Concatenating the popular features together and forming a high dimensional feature space introduces the curse of dimensionality problem. For this reason, dimensionality reduction techniques such as Principal Component Analysis is utilized on the feature space. In this study, first, some of the popular feature sets used in face recognition literature are evaluated over three popular face databases, namely ORL, UMIST, and Yale. Then, high dimensional feature space obtained by concatenating all the features is reduced to a lower dimensional space by using the Minimal Redundancy Maximal Relevance feature selection method in order to design a generic and successful feature set. The results indicate that mRMR selects a small number of features which are satisfactory and consistent in terms of recognition performance, provided that the face database is statistically stable with sufficient amount of data.
机译：人脸识别中的一个重要问题是代表人脸的特征空间的设计。为此目的已经并且正在不断提出各种特征集。但是，不存在可以在各种面部数据库上提供卓越且一致的识别性能的功能集。将流行特征连接在一起并形成高维特征空间会引入维数问题的诅咒。因此，在特征空间上使用了降维技术（例如主成分分析）。在这项研究中，首先，通过三个流行的面部数据库，即ORL，UMIST和Yale，对面部识别文献中使用的一些流行特征集进行了评估。然后，通过使用最小冗余最大相关性特征选择方法，将所有特征连接在一起获得的高维特征空间减少到较低维空间，以设计通用且成功的特征集。结果表明，只要面部数据库在统计上稳定且具有足够的数据量，mRMR会选择少量在识别性能方面令人满意且一致的特征。

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