Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?

Research output: Contribution to conferencePoster

Abstract

Facial Expressions are one of the main methods we use to express our emotions to others. Yet Facial Expression Recognition (FER) remains a difficult topic for machines to intrepret. While Computer Vision can extract features quite easily from imagery, there is still the difficult step of recognizing what emotion those features belong to. Many have taken to Deep Learning to bridge this learning gap. However this paper shows that with selected features, even classic techniques without modification can achieve high accuracy. This paper demonstrates how select features, taken from ANOVA, LDA and PCA, enhances the accuracy of HOG without further processes.
LanguageEnglish
Number of pages8
Publication statusAccepted/In press - 6 Jul 2018
EventIrish Machine Vision and Image Processing Conference - Belfast, United Kingdom
Duration: 29 Aug 201831 Aug 2018

Conference

ConferenceIrish Machine Vision and Image Processing Conference
Abbreviated titleIMVIP
CountryUnited Kingdom
Period29/08/1831/08/18

Fingerprint

Analysis of variance (ANOVA)
Computer vision
Feature extraction
Classifiers
Deep learning

Keywords

  • Facial Expression Recognition
  • Feature Selection
  • Feature Reduction
  • Machine Vision
  • Machine learning

Cite this

Melaugh, R., Siddique, N., Coleman, S., & Pratheepan, Y. (Accepted/In press). Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?. Poster session presented at Irish Machine Vision and Image Processing Conference, United Kingdom.
Melaugh, Ryan ; Siddique, N ; Coleman, Sonya ; Pratheepan, Y. / Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?. Poster session presented at Irish Machine Vision and Image Processing Conference, United Kingdom.8 p.
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abstract = "Facial Expressions are one of the main methods we use to express our emotions to others. Yet Facial Expression Recognition (FER) remains a difficult topic for machines to intrepret. While Computer Vision can extract features quite easily from imagery, there is still the difficult step of recognizing what emotion those features belong to. Many have taken to Deep Learning to bridge this learning gap. However this paper shows that with selected features, even classic techniques without modification can achieve high accuracy. This paper demonstrates how select features, taken from ANOVA, LDA and PCA, enhances the accuracy of HOG without further processes.",
keywords = "Facial Expression Recognition, Feature Selection, Feature Reduction, Machine Vision, Machine learning",
author = "Ryan Melaugh and N Siddique and Sonya Coleman and Y Pratheepan",
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language = "English",
note = "Irish Machine Vision and Image Processing Conference, IMVIP ; Conference date: 29-08-2018 Through 31-08-2018",

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Melaugh, R, Siddique, N, Coleman, S & Pratheepan, Y 2018, 'Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?' Irish Machine Vision and Image Processing Conference, United Kingdom, 29/08/18 - 31/08/18, .

Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection? / Melaugh, Ryan; Siddique, N; Coleman, Sonya; Pratheepan, Y.

2018. Poster session presented at Irish Machine Vision and Image Processing Conference, United Kingdom.

Research output: Contribution to conferencePoster

TY - CONF

T1 - Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?

AU - Melaugh, Ryan

AU - Siddique, N

AU - Coleman, Sonya

AU - Pratheepan, Y

PY - 2018/7/6

Y1 - 2018/7/6

N2 - Facial Expressions are one of the main methods we use to express our emotions to others. Yet Facial Expression Recognition (FER) remains a difficult topic for machines to intrepret. While Computer Vision can extract features quite easily from imagery, there is still the difficult step of recognizing what emotion those features belong to. Many have taken to Deep Learning to bridge this learning gap. However this paper shows that with selected features, even classic techniques without modification can achieve high accuracy. This paper demonstrates how select features, taken from ANOVA, LDA and PCA, enhances the accuracy of HOG without further processes.

AB - Facial Expressions are one of the main methods we use to express our emotions to others. Yet Facial Expression Recognition (FER) remains a difficult topic for machines to intrepret. While Computer Vision can extract features quite easily from imagery, there is still the difficult step of recognizing what emotion those features belong to. Many have taken to Deep Learning to bridge this learning gap. However this paper shows that with selected features, even classic techniques without modification can achieve high accuracy. This paper demonstrates how select features, taken from ANOVA, LDA and PCA, enhances the accuracy of HOG without further processes.

KW - Facial Expression Recognition

KW - Feature Selection

KW - Feature Reduction

KW - Machine Vision

KW - Machine learning

M3 - Poster

ER -

Melaugh R, Siddique N, Coleman S, Pratheepan Y. Feature Selection, Reduction and Classifiers using Histogram of Oriented Gradients: How important is Feature selection?. 2018. Poster session presented at Irish Machine Vision and Image Processing Conference, United Kingdom.