Facial Emotion Recognition using Deep Learning (FERDL)

Facial Emotion Recognition is one of the in-demand and rapidly growing research topics in the domain of Computer Vision (CV) and artificial intelligence (AI). The ability to identify or detect human emotions from real-time facial expressions (FEs) has vast conceivable applications in different domains, such as sentiment analysis, human-computer interaction, human resource management, security, and human psychology. In this paper, a Convolutional Neural Network (CNN) based deep learning model is trained with haar-cascade classifier to recognize the real-time FEs. The suggested model is specially trained to categorize the FEs into one of the seven emotion categories, namely six basic emotions (sad, happy, angry, surprised, disgusted, fear) and a neutral emotion. It includes several convolutional layers, as well as fully connected neurons followed by max-pooling layers, and soft-max activation function with the corresponding seven classes. ReLU activation functions along with various kernels to enhance filtering depth, and extraction of facial features. FER-2013 dataset is used for experimentation purpose. To improve the classification performance and model accuracy, a data augmentation technique is used for rescaling and horizontal flipping. The proposed model outperforms the previous related works by achieving a validation accuracy of 71.96% and training accuracy above 90%, with fewer epochs.

I. Introduction

Out of the fast-expanding areas of research in the domain of computer vision (CV) and machine learning (ML) is human face emotion recognition. There are many possible uses for the capability to identify and decipher human emotions [1] from FEs, including in the fields of psychology, marketing, security surveillance, crowd analytics, and human-computer interaction (HCI) etc.