Authors :
Subodh Lonkar
Volume/Issue :
Volume 6 - 2021, Issue 7 - July
Google Scholar :
http://bitly.ws/9nMw
Scribd :
https://bit.ly/37socEx
Abstract :
Over the centuries, humans have developed
and acquired a number of ways to communicate. But
hardly any of them can be as natural and instinctive as
facial expressions. On the other hand, neural networks
have taken the world by storm. And no surprises, that the
area of Computer Vision and the problem of facial
expressions recognitions hasn't remained untouched.
Although a wide range of techniques have been applied,
achieving extremely high accuracies and preparing highly
robust FER systems still remains a challenge due to
heterogeneous details in human faces. In this paper, we
will be deep diving into implementing a system for
recognition of facial expressions (FER) by leveraging
neural networks, and more specifically, Convolutional
Neural Networks (CNNs). We adopt the fundamental
concepts of deep learning and computer vision with
various architectures, fine-tune it's hyperparameters and
experiment with various optimization methods and
demonstrate a state-of-the-art single-network-accuracy of
70.10% on the FER2013 dataset without using any
additional training data.
Keywords :
Computer Vision; Cnn; Fer2013; Facial Expressions Recognition; Fer
Over the centuries, humans have developed
and acquired a number of ways to communicate. But
hardly any of them can be as natural and instinctive as
facial expressions. On the other hand, neural networks
have taken the world by storm. And no surprises, that the
area of Computer Vision and the problem of facial
expressions recognitions hasn't remained untouched.
Although a wide range of techniques have been applied,
achieving extremely high accuracies and preparing highly
robust FER systems still remains a challenge due to
heterogeneous details in human faces. In this paper, we
will be deep diving into implementing a system for
recognition of facial expressions (FER) by leveraging
neural networks, and more specifically, Convolutional
Neural Networks (CNNs). We adopt the fundamental
concepts of deep learning and computer vision with
various architectures, fine-tune it's hyperparameters and
experiment with various optimization methods and
demonstrate a state-of-the-art single-network-accuracy of
70.10% on the FER2013 dataset without using any
additional training data.
Keywords :
Computer Vision; Cnn; Fer2013; Facial Expressions Recognition; Fer