Normal view MARC view ISBD view

FERNet: a deep CNN Architecture for facial expression recognition in the wild

By: Bodapati, Jyostna Devi.
Contributor(s): Srilakshmi, U.
Publisher: New York Springer 2022Edition: Vol.103(2), Apr.Description: 439-448p.Subject(s): Humanities and Applied SciencesOnline resources: Click here In: Journal of the institution of engineers (India): Series BSummary: Facial expression recognition is an intriguing and demanding subject in the realm of computer vision. In this paper, we propose a novel deep learning-based strategy to address the challenges of facial expression recognition from images. Our model is developed in such a manner that it learns hidden nonlinearity from the input facial images, which is critical for discriminating the type of emotion a person is expressing. We developed a deep convolutional neural network model composed of a sequence of blocks, each consists of multiple convolutional layers and sub-sampling layers. Investigations on the benchmark FER2013 dataset indicate that the proposed facial expression recognition network (FERNet) surpasses existing approaches in terms of performance and model complexity. We trained our model on the FER2013 dataset, which is the most challenging of all the available datasets for this task, and achieve an accuracy of around 69.57%. Furthermore, we investigate the effects of dropout, batch normalization, and augmentation, as well as how they aid in the reduction of over-fitting and improved performance.
Tags from this library: No tags from this library for this title. Log in to add tags.
    average rating: 0.0 (0 votes)
Item type Current location Call number Status Date due Barcode Item holds
Articles Abstract Database Articles Abstract Database School of Engineering & Technology
Archieval Section
Not for loan 2022-1610
Total holds: 0

Facial expression recognition is an intriguing and demanding subject in the realm of computer vision. In this paper, we propose a novel deep learning-based strategy to address the challenges of facial expression recognition from images. Our model is developed in such a manner that it learns hidden nonlinearity from the input facial images, which is critical for discriminating the type of emotion a person is expressing. We developed a deep convolutional neural network model composed of a sequence of blocks, each consists of multiple convolutional layers and sub-sampling layers. Investigations on the benchmark FER2013 dataset indicate that the proposed facial expression recognition network (FERNet) surpasses existing approaches in terms of performance and model complexity. We trained our model on the FER2013 dataset, which is the most challenging of all the available datasets for this task, and achieve an accuracy of around 69.57%. Furthermore, we investigate the effects of dropout, batch normalization, and augmentation, as well as how they aid in the reduction of over-fitting and improved performance.

There are no comments for this item.

Log in to your account to post a comment.

Click on an image to view it in the image viewer

Unique Visitors hit counter Total Page Views free counter
Implemented and Maintained by AIKTC-KRRC (Central Library).
For any Suggestions/Query Contact to library or Email: librarian@aiktc.ac.in | Ph:+91 22 27481247
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha