Multi-pose facial expression recognition using appearance-based facial features Online publication date: Wed, 09-Sep-2020
by Yogesh Kumar; Shashi Kant Verma; Sandeep Sharma
International Journal of Intelligent Information and Database Systems (IJIIDS), Vol. 13, No. 2/3/4, 2020
Abstract: The problem of automatic facial expression recognition is both interesting and challenging with a strong impact on many application areas such as animation and human-computer interaction. The field has shown tremendous growth over the past years with its benchmarking efforts and progress. In this paper, an automated system to recognise facial expressions using the deep convolutional neural network is presented. The proposed system has used the appearance based features to recognise the facial expressions from imagery data with pose variations. The appearance based features are extracted by implementing an integrated approach of Gabor filter with local binary pattern method and the selection process of extracted features is executed using the concept of principal component analysis (PCA). The proposed system performance is accessed using the evaluation metrics of precision, recall, f-measure, and recognition rate for the frontal and half side pose images of the JAFFE and KDEF datasets.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Information and Database Systems (IJIIDS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook