Facial Emotions Recognition using Convolutional Neural Net

TL;DR

This paper presents a CNN-based system for recognizing seven basic facial emotions, utilizing data augmentation, face detection, and real-time classification with an accuracy of 78.1%.

Contribution

It introduces a novel CNN architecture combined with data preprocessing and augmentation for improved facial emotion recognition.

Findings

Achieved 78.1% accuracy on combined datasets.

Implemented real-time emotion classification with GUI.

Enhanced prediction through data augmentation and preprocessing.

Abstract

Facial expressions vary from person to person, and the brightness, contrast, and resolution of every random image are different. This is why recognizing facial expressions is very difficult. This article proposes an efficient system for facial emotion recognition for the seven basic human emotions (angry, disgust, fear, happy, sad, surprise, and neutral), using a convolution neural network (CNN), which predicts and assigns probabilities to each emotion. Since deep learning models learn from data, thus, our proposed system processes each image with various pre-processing steps for better prediction. Every image was first passed through the face detection algorithm to include in the training dataset. As CNN requires a large amount of data, we duplicated our data using various filters on each image. Pre-processed images of size 80*100 are passed as input to the first layer of CNN. Three convolutional layers were used, followed by a pooling layer and three dense layers. The dropout rate for the dense layer was 20%. The model was trained by combining two publicly available datasets, JAFFE and KDEF. 90% of the data was used for training, while 10% was used for testing. We achieved maximum accuracy of 78.1 % using the combined dataset. Moreover, we designed an application of the proposed system with a graphical user interface that classifies emotions in real-time.