Multiplex Image Representation for Enhanced Recognition

X Wei, H. Wang, Gongde Guo, Huan Wan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.
LanguageEnglish
Pages383–392
Number of pages10
JournalInternational Journal of Machine Learning and Cybernetics
Volume9
Issue number3
Early online date21 Sep 2015
DOIs
Publication statusPublished - 31 Mar 2018

Fingerprint

Palmprint recognition
Face recognition
Ubiquitous computing
Joining
Learning algorithms
Learning systems
Feature extraction

Keywords

  • Multiplex image representation
  • Feature fusion
  • LBP
  • HOG
  • Face recognition

Cite this

@article{99beadf0dd48455fba8543afc92cc63a,
title = "Multiplex Image Representation for Enhanced Recognition",
abstract = "A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.",
keywords = "Multiplex image representation, Feature fusion, LBP, HOG, Face recognition",
author = "X Wei and H. Wang and Gongde Guo and Huan Wan",
year = "2018",
month = "3",
day = "31",
doi = "10.1007/s13042-015-0427-5",
language = "English",
volume = "9",
pages = "383–392",
journal = "International Journal of Machine Learning and Cybernetics",
issn = "1868-8071",
number = "3",

}

Multiplex Image Representation for Enhanced Recognition. / Wei, X; Wang, H.; Guo, Gongde; Wan, Huan.

In: International Journal of Machine Learning and Cybernetics, Vol. 9, No. 3, 31.03.2018, p. 383–392.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Multiplex Image Representation for Enhanced Recognition

AU - Wei, X

AU - Wang, H.

AU - Guo, Gongde

AU - Wan, Huan

PY - 2018/3/31

Y1 - 2018/3/31

N2 - A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.

AB - A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.

KW - Multiplex image representation

KW - Feature fusion

KW - LBP

KW - HOG

KW - Face recognition

U2 - 10.1007/s13042-015-0427-5

DO - 10.1007/s13042-015-0427-5

M3 - Article

VL - 9

SP - 383

EP - 392

JO - International Journal of Machine Learning and Cybernetics

T2 - International Journal of Machine Learning and Cybernetics

JF - International Journal of Machine Learning and Cybernetics

SN - 1868-8071

IS - 3

ER -