Robust Visual Similarity Retrieval in Single Model Face Databases

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Title Robust Visual Similarity Retrieval in Single Model Face Databases
Author Gao, Yongsheng; Qi, Yutao
Journal Name Pattern Recognition
Editor Dr.Robert S Ledley
Year Published 2005
Place of publication UK
Publisher Elsevier / Pergamon
Abstract In this paper, we introduce a novel visual similarity measuring technique to retrieve face images in photo album databases for law enforcement. Though much work is being done on face similarity matching techniques, little attention is given to the design of face matching schemes suitable for visual retrieval in single model databases where accuracy, robustness to scale and environmental changes, and computational efficiency are three important issues to be considered. This paper presents a robust face retrieval approach using structural and spatial point correspondence in which the directional corner points (DCPs) are generated for efficient face coding and retrieval. A complete investigation on the proposed method is conducted, which covers face retrieval under controlled/ideal condition, scale variations, environmental changes and subject actions. The system performance is compared with the performance of the eigenface method. It is an attractive finding that the proposed DCP retrieval technique has performed superior to the eigenface method in most of the comparison experiments. This research demonstrates that the proposed DCP approach provides a new way, which is both robust to scale and environmental changes, and efficient in computation, for retrieving human faces in single model databases.
Peer Reviewed Yes
Published Yes
Alternative URI
Volume 38
Page from 1009
Page to 1020
ISSN 0031-3203
Date Accessioned 2005-12-12
Date Available 2015-05-06T21:37:12Z
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Engineering and Information Technology
Subject PRE2009-Pattern Recognition
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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