Gender Classification using Interlaced Derivative Patterns

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Shobeirinejad, A
Gao, Y
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IAPR
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2010
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1192628 bytes
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Istanbul, Turkey
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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).

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Proceedings - International Conference on Pattern Recognition
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© 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.
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Computer vision
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