Multi-PIE
Multi-PIE
复制标题
DOI：
10.1016/j.imavis.2009.08.002
复制
发表时间：
2010-05-01
影响因子：
4.7
通讯作者：
Baker, Simon
中科院分区：
文献类型：
--
作者：
Gross, Ralph;Matthews, Iain;Baker, Simon
人脸识别算法的进步与以可控方式包含影响面部外观的各种因素的人脸数据库的可用性之间存在密切关系。卡内基梅隆大学的PIE数据库在推动跨姿态和光照的人脸识别研究方面具有很大影响力。尽管它很成功，但PIE数据库存在一些缺点：受试者数量有限、只有一次录制过程且只捕捉到了少数几种表情。为了解决这些问题，我们收集了卡内基梅隆大学的多姿态和光照变化的人脸（Multi - PIE）数据库。它包含337个受试者，在多达四次的录制过程中，在15个视角和19种光照条件下成像。在本文中，我们介绍了该数据库并描述了录制过程。我们还展示了使用主成分分析（PCA）和线性判别分析（LDA）分类器进行的基线实验的结果，以突出PIE和Multi - PIE之间的相似性和差异。(C) 2009爱思唯尔有限公司。保留所有权利。
A close relationship exists between the advancement of face recognition algorithms and the availability of face databases varying factors that affect facial appearance in a controlled manner. The CMU PIE database has been very influential in advancing research in face recognition across pose and illumination. Despite its success the PIE database has several shortcomings: a limited number of subjects, a single recording session and only few expressions captured. To address these issues we collected the CMU Multi-PIE database. It contains 337 subjects, imaged under 15 view points and 19 illumination conditions in up to four recording sessions. In this paper we introduce the database and describe the recording procedure. We furthermore present results from baseline experiments using PCA and LDA classifiers to highlight similarities and differences between PIE and Multi-PIE. (C) 2009 Elsevier B.V. All rights reserved.