Authors: Yogesh Kumar; Shashi Kant Verma; Sandeep Sharma
Addresses: Department of Computer Science and Engineering, Uttarakhand Technical University, Dehradun, Uttarakhand, India ' Department of Computer Science and Engineering, Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand, India ' Department of Electronics and Communication Engineering, DIT University, Dehradun, Uttarakhand, India
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.
Keywords: facial expressions; appearance-based facial expressions; deep convolutional neural network; DCNN; multi-pose face appearance.
International Journal of Intelligent Information and Database Systems, 2020 Vol.13 No.2/3/4, pp.172 - 190
Received: 27 Mar 2019
Accepted: 23 Jul 2019
Published online: 25 Aug 2020 *