Coleman, Sonya

Melaugh, Ryan

Pratheepan, Y

Siddique, N

English

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

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

https://pure.ulster.ac.uk/ws/files/12590198/imvip2018_RyanMelaugh_SubmissionFinal.pdf

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

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