Gender Classification using Interlaced Derivative Patterns

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Title Gender Classification using Interlaced Derivative Patterns
Author Shobeirinejad, Ameneh; Gao, Yongsheng
Publication Title Proceedings of the 20th International Conference on Pattern Recognition (ICPR 2010)
Editor IAPR
Year Published 2010
Place of publication United States
Publisher IEEE Computer Society
Abstract Automated gender recognition has become an interesting and challenging research problem in recent years with its potential applications in security industry and human-computer interaction systems. In this paper we present a novel feature representation, namely Interlaced Derivative Patterns (IDP), which is a derivative-based technique to extract discriminative facial features for gender classification. The proposed technique operates on a neighborhood around a pixel and concatenates the extracted regional feature distributions to form a feature vector. The experimental results demonstrate the effectiveness of the IDP method for gender classification, showing that the proposed approach achieves 29.6% relative error reduction compared to Local Binary Patterns (LBP), while it performs over four times faster than Local Derivative Patterns (LDP).
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1109/ICPR.2010.1118
Copyright Statement Copyright 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
ISBN 1051-4651
Conference name The 20th International Conference on Pattern Recognition (ICPR 2010)
Location Istanbul, Turkey
Date From 2010-08-23
Date To 2010-08-26
URI http://hdl.handle.net/10072/37204
Date Accessioned 2010-12-07
Date Available 2011-04-18T06:56:05Z
Language en_AU
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
Publication Type Conference Publications (Full Written Paper - Refereed)
Publication Type Code e1

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