caa a22 4500 605447527 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1803-3 doi (NATIONALLICENCE)springer-10.1007/s11042-013-1803-3 An adaptive training based on classification system for patterns in facial expressions using SURF descriptor templates [Elektronische Daten] [M. Zia, M. Jaffar] Most facial expression recognition (FER) systems used facial expression data created during a short period of time and this data is used for learning/training of FER systems. There are many facial expression patterns (i.e. a particular expression can be represented in many different patterns) which cannot be generated and used as learning/training data in a short time. Therefore, in order to maintain its high accuracy and robustness for a long time of a facial expression recognition system, the classifier should be evolved adaptively over time and space. We proposed a facial expression recognition system that has the aptitude of incrementally learning and thus can learn all possible patterns of expressions that may be generated in feature. After extraction of region of interest (face), the system extracts Speeded-Up Robust Features (SURF). A novel SURF descriptor template based nearest neighbor classifier is proposed for classification. This classifier is used as base/weak classifier for incremental learning algorithm Learn++. A vast range of experimentation is performed on five different databases that demonstrate the incremental learning capability of the proposed system. The experiments using the incrementally learning classification demonstrate promising results. Springer Science+Business Media New York, 2013 Facial expressions recognition nationallicence Incremental learning nationallicence SURF nationallicence Template matching nationallicence Machine learning nationallicence Zia M. National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan aut Jaffar M. National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3881-3899 1380-7501 74:11<3881 2015 74 11042 https://doi.org/10.1007/s11042-013-1803-3 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1803-3 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zia M. National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan aut NATIONALLICENCE 700 1- Jaffar M. National University of Computer and Emerging Sciences, Islamabad, Islamabad, Pakistan aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3881-3899 1380-7501 74:11<3881 2015 74 11042