Multi-PIE

被引：1307

作者：

Gross, Ralph ^{[1
]}

Matthews, Iain ^{[1
]}

Cohn, Jeffrey ^{[2
]}

Kanade, Takeo ^{[1
]}

Baker, Simon ^{[3
]}

机构：

[1] Carnegie Mellon Univ, Inst Robot, Pittsburgh, PA 15213 USA

[2] Univ Pittsburgh, Dept Psychol, Pittsburgh, PA 15260 USA

[3] Microsoft Corp, Microsoft Res, Redmond, WA 98052 USA

来源：

IMAGE AND VISION COMPUTING | 2010年 / 28卷 / 05期

关键词：

Face database; Face recognition across pose; Face recognition across illumination; Face recognition across expression; FACE-RECOGNITION; ILLUMINATION; EIGENFACES; MODELS;

D O I：

10.1016/j.imavis.2009.08.002

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：807 / 813

页数：7

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