Resource-Allocating Codebook for Patch-based Face Recognition
Resource-Allocating Codebook for Patch-based Face Recognition
In this paper we propose a novel approach to constructing a discriminant visual codebook in a simple and extremely fast way as a one-pass, that we call Resource-Allocating Codebook (RAC), inspired by the Resource Allocating Network (RAN) algorithms developed in the artificial neural networks literature. Unlike density preserving clustering, this approach retains data spread out more widely in the input space, thereby including rare low level features in the codebook. We show that the codebook constructed by the RAC technique outperforms the codebook constructed by K-means clustering in recognition performance and computation on two standard face databases, namely the AT&T and Yale faces, performed with SIFT features.
Cluster analysis, Codebook, Face recognition, SIFT
28-31
Ramanan, Amirthalingam
4b287910-5234-42ef-83f0-d9875c319a56
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
28 December 2009
Ramanan, Amirthalingam
4b287910-5234-42ef-83f0-d9875c319a56
Niranjan, Mahesan
5cbaeea8-7288-4b55-a89c-c43d212ddd4f
Ramanan, Amirthalingam and Niranjan, Mahesan
(2009)
Resource-Allocating Codebook for Patch-based Face Recognition.
Fourth International Conference on Industrial and Information Systems, Sri Lanka.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
In this paper we propose a novel approach to constructing a discriminant visual codebook in a simple and extremely fast way as a one-pass, that we call Resource-Allocating Codebook (RAC), inspired by the Resource Allocating Network (RAN) algorithms developed in the artificial neural networks literature. Unlike density preserving clustering, this approach retains data spread out more widely in the input space, thereby including rare low level features in the codebook. We show that the codebook constructed by the RAC technique outperforms the codebook constructed by K-means clustering in recognition performance and computation on two standard face databases, namely the AT&T and Yale faces, performed with SIFT features.
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Published date: 28 December 2009
Venue - Dates:
Fourth International Conference on Industrial and Information Systems, Sri Lanka, 2009-12-28
Keywords:
Cluster analysis, Codebook, Face recognition, SIFT
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 271401
URI: http://eprints.soton.ac.uk/id/eprint/271401
PURE UUID: 49ed779d-5b75-49f6-b5e7-7eb93f2e2e19
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Date deposited: 14 Jul 2010 12:23
Last modified: 15 Mar 2024 03:29
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Contributors
Author:
Amirthalingam Ramanan
Author:
Mahesan Niranjan
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