Simplified Gabor wavelets for efficient feature extraction

Wing Pong Choi; Siu Hong Tse; Kwok Wai Wong; Kin Man Lam

doi:10.1109/TENCON.2007.4428989

Simplified Gabor wavelets for efficient feature extraction

Wing Pong Choi, Siu Hong Tse, Kwok Wai Wong, Kin Man Lam

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).

Original language	English
Title of host publication	TENCON 2007 - 2007 IEEE Region 10 Conference
DOIs	https://doi.org/10.1109/TENCON.2007.4428989
Publication status	Published - 1 Dec 2007
Event	IEEE Region 10 Conference, TENCON 2007 - Taipei, Taiwan Duration: 30 Oct 2007 → 2 Nov 2007

Conference

Conference	IEEE Region 10 Conference, TENCON 2007
Country/Territory	Taiwan
City	Taipei
Period	30/10/07 → 2/11/07

ASJC Scopus subject areas

Computer Science Applications
Electrical and Electronic Engineering

More information

10.1109/TENCON.2007.4428989

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@inproceedings{8e8dd395d89c418493edc0d22be81b07,

title = "Simplified Gabor wavelets for efficient feature extraction",

abstract = "Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).",

author = "Choi, {Wing Pong} and Tse, {Siu Hong} and Wong, {Kwok Wai} and Lam, {Kin Man}",

year = "2007",

month = dec,

day = "1",

doi = "10.1109/TENCON.2007.4428989",

language = "English",

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booktitle = "TENCON 2007 - 2007 IEEE Region 10 Conference",

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T1 - Simplified Gabor wavelets for efficient feature extraction

AU - Choi, Wing Pong

AU - Tse, Siu Hong

AU - Wong, Kwok Wai

AU - Lam, Kin Man

PY - 2007/12/1

Y1 - 2007/12/1

N2 - Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).

AB - Gabor wavelets (GWs) are commonly used for extracting local features for various applications like object detection, recognition and tracking. However, extracting Gabor features is computationally intensive, so the features are impractical for real-time applications. In this paper, we propose a simplified version of Gabor wavelets (SGWs) and an efficient algorithm for extracting the features based on an integral image. We evaluate the performance of the SGWs for face recognition. Experimental results show that SGWs can achieve a performance level similar to GWs, while the runtime for feature extraction using SGWs is about 4.39 times faster than that of GWs, implemented by using fast Fourier transform (FFT).

UR - http://www.scopus.com/inward/record.url?scp=48749090563&partnerID=8YFLogxK

U2 - 10.1109/TENCON.2007.4428989

DO - 10.1109/TENCON.2007.4428989

M3 - Conference article published in proceeding or book

SN - 1424412722

SN - 9781424412723

BT - TENCON 2007 - 2007 IEEE Region 10 Conference

T2 - IEEE Region 10 Conference, TENCON 2007

Y2 - 30 October 2007 through 2 November 2007

ER -

Simplified Gabor wavelets for efficient feature extraction

Abstract

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ASJC Scopus subject areas

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