Multi-pose facial expression recognition using appearance-based facial features. (25th August 2020)

• Record Type: Journal Article
• Authors: Kumar, Yogesh; Verma, Shashi Kant; Sharma, Sandeep
• Is Part Of: International journal of intelligent information and database systems. Volume 13:Number 2/4(2020)
• Page Start: 172
• Page End: 190
• Publication Date: 2020-08-25
• Subjects: facial expressions -- appearance-based facial expressions -- deep convolutional neural network -- DCNN -- multi-pose face appearance; Database management -- Computer programs -- Periodicals; Information retrieval -- Computer programs -- Periodicals; Information storage and retrieval systems -- Computer programs -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligent agents (Computer software) -- Periodicals; 006.33
• Languages: English
• ISSNs: 1751-5858

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.