|Autors: Tonchev, K., Neshov, N. N., Manolova, A. H., Poulkov, V. K.|
Title: Expression Recognition Using Sparse Selection of log-Gabor Facial Features
Keywords: Feature extraction; Approximation algorithms; Face recognition; Support vector machines; Dimensionality reduction; Machine learning algorithms; Task analysis
Abstract: Automated expression recognition is a contemporary research field estimating human expressions from image or video data using computer algorithms combined with machine learning. This work proposes an algorithm for expression recognition including a feature extraction algorithm, consisting of log-Gabor filters followed by a feature selection based on sparse approximation of graph embedding. The classification is done on the selected features and is implemented using the Support Vector Machines classifier with radial basis kernel function. The algorithm is tested on the posed facial expressions image database Cohn-Kanade and provides competitive results compared to the state of the art.
1. Han, Z., Huang, H., Wang, J., "Convolutional Neural Network Based Expression Classification with Face Alignment", 2018 International Conference on Information, Cybernetics, and Computational Social Systems, ICCSS 2018, pp. 408-412, 2018, DOI: 10.1109/ICCSS.2018.8572342. - 2018 - в издания, индексирани в Scopus или Web of Science
Вид: публикация в международен форум, публикация в реферирано издание, индексирана в Scopus