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Face Recognition Using Gabor-Based Feature Extraction and Feature Space Transformation Fusion Method for Single Image per Person Problem

Authors:

Yixin ZhangAuthors Info & Claims

Neural Processing Letters, Volume 47, Issue 3

Pages 1197 - 1217

https://doi.org/10.1007/s11063-017-9693-4

Published: 01 June 2018 Publication History

Abstract

Discriminant analysis technique plays an important role in face recognition because it can extract discriminative features to classify different persons. However, most existing discriminant analysis methods fail to work for single image per person problem (SIPPP) because there is only a single training sample per person such that the within-class variation of this person cannot be calculated in such case. In this paper, we present a new face recognition method for SIPPP. The method is a combination of Gabor wavelets, feature space transformation (FST) based on fusion feature matrix, and nearest neighbor classifier (NNc). First, we use Gabor wavelets to extract the feature vectors from a raw training sample image, because Gabor-based features are more robust than spectral-based features and could avoid the local distortions caused by the variance of expression, pose, light and noise. Then, the extracted spatial-based feature vectors and spectral-based feature vectors are combined, and projected to a low-dimensional subspace by using dimensionality reduction techniques. Finally, the classification can be completed via using NNc. The proposed method is abbreviated as G-FST. The performance of G-FST method is evaluated on ORL, Yale and FERET databases. Experimental results show that the G-FST method outperforms the other related methods in terms of recognition rates and recognition time.

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Cited By

Qiao LLiu KXue YTang WSalehnia T(2024)A multi-level thresholding image segmentation method using hybrid Arithmetic Optimization and Harris Hawks Optimizer algorithmsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122316241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122316
Karanwal S(2024)Discriminative binary pattern descriptor for face recognitionPattern Analysis & Applications10.1007/s10044-024-01293-w27:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/s10044-024-01293-w
Juneja K(2023)Design of a multi-stage hybrid model for face recognition in varied illumination conditionsMultimedia Tools and Applications10.1007/s11042-022-13586-582:4(5627-5662)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-022-13586-5
Show More Cited By

Face Recognition Using Gabor-Based Feature Extraction and Feature Space Transformation Fusion Method for Single Image per Person Problem
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Machine learning algorithms

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Published In

cover image Neural Processing Letters

Neural Processing Letters Volume 47, Issue 3

June 2018

525 pages

ISSN:1370-4621

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2018

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Cited By

Qiao LLiu KXue YTang WSalehnia T(2024)A multi-level thresholding image segmentation method using hybrid Arithmetic Optimization and Harris Hawks Optimizer algorithmsExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.122316241:COnline publication date: 1-May-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.122316
Karanwal S(2024)Discriminative binary pattern descriptor for face recognitionPattern Analysis & Applications10.1007/s10044-024-01293-w27:3Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1007/s10044-024-01293-w
Juneja K(2023)Design of a multi-stage hybrid model for face recognition in varied illumination conditionsMultimedia Tools and Applications10.1007/s11042-022-13586-582:4(5627-5662)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-022-13586-5
Zhou DYang DZhang XHuang SFeng S(2019)Discriminative Probabilistic Latent Semantic Analysis with Application to Single Sample Face RecognitionNeural Processing Letters10.1007/s11063-018-9852-249:3(1273-1298)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1007/s11063-018-9852-2

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