Symbolic Aggregate approXimation-Local Binary Pattern Feature Descriptor Combination for Automatic Facial Expression Recognition

Sumithra ,M Abdul Rahiman

doi:10.3844/jcssp.2019.45.56

Abstract

Automatic identification of facial expression is a significant research area which is anticipated for real time processing in Human-Computer Interaction domain. Along with an efficient classifier for assigning the class label to each of the input face image, it is very necessary to have a strong feature vector for training the classifier. This paper proposes an effectual combination of Local Binary Pattern and Symbolic Aggregate approXimation method for the feature vector generation for the classifier. Twenty one facial patches are extracted from the face image and the LBP value and SAX string for these twenty one patches are utilised for feature vector generation. The feature vectors of images are submitted to the Ensemble Bag classifier for training purpose. Images which were not used for training is used for testing. An average accuracy of 98.7% was obtained when tested on JAFFE data set for seven expressions and an accuracy of 96.96% was obtained for nine expressions on fused database. A detailed analysis of the testing conducted on images with partial occlusion and illumination variance are presented here.

Highlights

Internet of Things (IoT) is undoubtedly an emerging field where the human and machine communication becomes a pivot element
The proposed work achieves an accuracy of 98.7% for recognition of seven expressions when evaluated on JAFFE database
An automatic facial recognition system for recognising nine facial expressions has been implemented which handles partial occlusion and can work effectively on images with illumination variance and which works on person independent images

Summary

Introduction

Internet of Things (IoT) is undoubtedly an emerging field where the human and machine communication becomes a pivot element. The digital era demands a high need of computations based on human thought processes. Human thoughts and emotions are communicated through the facial expressions. Affective computing makes communication of human thoughts to the machines effectively (Cruz et al, 2014). Ekman and Friesen (1971) published a paper titled “Constants across cultures in the face and emotions” in which the six facial emotions of happiness, sadness, anger, fear, surprise and disgust are suggested as universal human expressions. Suwa et al (1978) in his paper presented his research work on the analysis of facial expressions from a sequence of facial images. The research in this field got geared up only after 1991

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