Automatic Multiface Expression Recognition Using Convolutional Neural Network

Padmapriya K.c Padmapriya K.C,Leelavathy V Leelavathy V,Angelin Gladston

doi:10.4018/ijaiml.20210701.oa8

Abstract

The human facial expressions convey a lot of information visually. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Most of the methods utilized in the literature for the automatic facial expression recognition systems are based on geometry and appearance. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. In this paper we applied various deep learning methods to classify the seven key human emotions: anger, disgust, fear, happiness, sadness, surprise and neutrality. The facial expression recognition system developed is experimentally evaluated with FER dataset and has resulted with good accuracy.

Highlights

Human facial expressions convey a lot of information visually
The sequential Gaussian mixture model and deep neural network (GMM-DNN) classifier has been contrasted with support vector machines (SVMs) and multilayer perceptron (MLP) classifiers, and its performance accuracy is indexed at 83.97%, while the other two perform at 80.33% and 69.78% using SVMs and MLP, respectively. These results demonstrated that the hybrid classifier significantly gives higher emotion recognition accuracy than SVMs and MLP classifiers
The dataset used for training the model is from a Kaggle Facial Expression Recognition Challenge a few years back (FER2013)

Summary

Introduction

Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications in human behavior understanding, detection of mental disorders, and synthetic human expressions. Facial expression recognition is usually performed in four stages consisting of pre-processing, face detection, feature extraction, and expression classification. Image Processing is a vast area of research in present day world and its applications are very widespread. One of the most important application of Image processing is Facial expression recognition. Facial Expressions plays an important role in interpersonal communication. Facial expression is a non-verbal scientific gesture which gets expressed in our face as per our emotions (Dai et al, 2019). Automatic recognition of facial expression plays an important role in artificial intelligence and robotics and it is a need of the generation.

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Conclusion