Improved HMM based face recognition system

Peter Corcoran

Improved HMM based face recognition system

Peter Corcoran

Electrical & Electronic Engineering

Research output: Chapter in Book or Conference Publication/Proceeding › Conference Publication › peer-review

Abstract

In this paper we present a face recognition system based on an embedded hidden Markov models, which uses an efficient set of observation vectors obtained from the 2D-DCT coefficients. Two dimensional data such as images are much better modeled by a two dimensional HMM compared to a one dimensional HMM, but the computational complexity of the first makes the recognition process difficult. The embedded HMM realizes a compromise between the two models: due to its pseudo two dimensional structure is able to model the two dimension data better than the one dimensional HMM and is computationally less complex than the two dimensional HMM. In order to improve the robustness of the recognition system to different illumination we apply an illumination normalization technique (CLAHE) prior to analysis.

Original language	English (Ireland)
Title of host publication	PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT, VOL IV
Publisher	TRANSILVANIA UNIV PRESS-BRASOV
Number of pages	3
Publication status	Published - 1 Jan 2006

Authors (Note for portal: view the doc link for the full list of authors)

Authors
Corcoran, P;Iancu, C;Costache, G

Cite this

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title = "Improved HMM based face recognition system",

abstract = "In this paper we present a face recognition system based on an embedded hidden Markov models, which uses an efficient set of observation vectors obtained from the 2D-DCT coefficients. Two dimensional data such as images are much better modeled by a two dimensional HMM compared to a one dimensional HMM, but the computational complexity of the first makes the recognition process difficult. The embedded HMM realizes a compromise between the two models: due to its pseudo two dimensional structure is able to model the two dimension data better than the one dimensional HMM and is computationally less complex than the two dimensional HMM. In order to improve the robustness of the recognition system to different illumination we apply an illumination normalization technique (CLAHE) prior to analysis.",

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AB - In this paper we present a face recognition system based on an embedded hidden Markov models, which uses an efficient set of observation vectors obtained from the 2D-DCT coefficients. Two dimensional data such as images are much better modeled by a two dimensional HMM compared to a one dimensional HMM, but the computational complexity of the first makes the recognition process difficult. The embedded HMM realizes a compromise between the two models: due to its pseudo two dimensional structure is able to model the two dimension data better than the one dimensional HMM and is computationally less complex than the two dimensional HMM. In order to improve the robustness of the recognition system to different illumination we apply an illumination normalization technique (CLAHE) prior to analysis.

M3 - Conference Publication

BT - PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON OPTIMIZATION OF ELECTRICAL AND ELECTRONIC EQUIPMENT, VOL IV

PB - TRANSILVANIA UNIV PRESS-BRASOV

ER -

Improved HMM based face recognition system

Abstract

Authors (Note for portal: view the doc link for the full list of authors)

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Cite this