Sampling Gabor features for face recognition

Dang Hui Liu; Kin Man Lam; Lan Sun Shen

doi:10.1109/ICNNSP.2003.1280751

Sampling Gabor features for face recognition

Dang Hui Liu, Kin Man Lam, Lan Sun Shen

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

1 Citation (Scopus)

Abstract

The Gabor feature is effective for facial image representation. However, the dimension of a Gabor feature vector is very high so that the computation and memory requirements are prohibitively large. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of Principal Component Analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.

Original language	English
Title of host publication	Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Pages	924-927
Number of pages	4
Volume	2
DOIs	https://doi.org/10.1109/ICNNSP.2003.1280751
Publication status	Published - 1 Dec 2003
Event	2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03 - Nanjing, China Duration: 14 Dec 2003 → 17 Dec 2003

Conference

Conference	2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03
Country/Territory	China
City	Nanjing
Period	14/12/03 → 17/12/03

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

More information

10.1109/ICNNSP.2003.1280751

Cite this

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abstract = "The Gabor feature is effective for facial image representation. However, the dimension of a Gabor feature vector is very high so that the computation and memory requirements are prohibitively large. In this paper, we propose a method to determine the optimal position for extracting the Gabor feature. The sub-sampled positions of the feature points are determined by a mask generated from a set of training images by means of Principal Component Analysis (PCA). With the feature vector of reduced dimension, a subspace LDA is applied for face recognition. Experimental results show that the new sampling method is simple, and effective for both dimension reduction and image representation. The recognition rate based on our proposed scheme is also higher than that achieved using a regular sampling method in a face region.",

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Sampling Gabor features for face recognition. / Liu, Dang Hui; Lam, Kin Man; Shen, Lan Sun.

Proceedings of 2003 International Conference on Neural Networks and Signal Processing, ICNNSP'03. Vol. 2 2003. p. 924-927 1280751.

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

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Sampling Gabor features for face recognition

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