Improved Model for Facial Expression Classification for Fear and Sadness Using Local Binary Pattern Histogram
Adebola K. Ojo, Temitope Ololade Idowu
Journal of Advances in Mathematics and Computer Science · pp. 22–33 · Published 24 Jul 2020
10.9734/jamcs/2020/v35i530279Abstract
In this study, a Local Binary Pattern Histogram model was proposed for Facial expression classification for fear and sadness. There have been a number of supervised machine models developed and used for facial recognition in past researches. The classifier requires human effort to perform feature extraction which has led to unknown changes in the expression of human face and incomplete feature extraction and low accuracy. This study proposed a model for improving the accuracies for fear and sadness and to extract features to distinguish between fear and sadness. Images of different people of varying ages were extracted from two datasets got from Japanese female facial expression (jaffe) dataset and Cohn cade got from Kaggle. In other to achieve an incremental development, classification was done using Linear Support Vector Machine (LSVM) and Random Forest Classifier (RFC). The accuracy rates for the LSVM models, LSVM1 and LSVM2 were 88% and 87% respectively while the RFC models, RFC1 and RFC2, were 81% and 82% respectively.
Cited by 6
Richa Rashmi, U. Snekhalatha, Anela L. Salvador · The Imaging Science Journal · 2023
Sumit Jain, Hare Ram Shah · Lecture Notes in Networks and Systems · 2023
Ankan Bhattacharyya, Somnath Chatterjee, Shibaprasad Sen · Scientific Reports · 2021
Showing 3 of 6 known citations — external sources report more than can currently be individually listed.
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