A novel spontaneous facial expression recognition using dynamically weighted majority voting based ensemble classifier M. Sultan Zia 1 & Majid Hussain 1 & M. Arfan Jaffar 2,3 Received: 30 April 2017 / Revised: 5 February 2018 / Accepted: 15 February 2018 # Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Facial Expression Recognition (FER) is inherently data driven. Spontaneous ex- pressions are substantially different from posed expressions. Spontaneous facial expressions are more challenging and more difficult to recognize. Any facial expression can be represented in many different patterns of muscles movements. Moreover, the facial expressions show discrepancies in different cultures and ethnicities. Therefore, a FER system has to learn a huge problem/feature space. A base classifier trained on a sub-region of feature space, cannot perform equally well in other areas of feature space. Therefore, a base classifier should be assigned higher voting weight in the regions near to its training space and a lower voting weight in the regions far from its training space. In order to maintain high accuracy and robustness of a FER system in space and time, a Dynamic Weight Majority Voting (DWMV) mechanism for base classifiers is introduced. An ensemble system for FER is proposed that has the aptitude of incrementally learning and thus, can learn all possible patterns of expressions that may be generated in feature or in various cultures and ethnicities. Speeded-Up Robust Features (SURF) are used to represent the feature space. Since no work in literature is found on Bwhich similarity measure is more appropriate in SURF descriptor domain for facial expres- sion recognition^, therefore, different similarity measures are used and the results are Multimed Tools Appl https://doi.org/10.1007/s11042-018-5806-y * M. Sultan Zia ziactn@ciitsahiwal.edu.pk Majid Hussain majidhussain@ciitsahiwal.edu.pk M. Arfan Jaffar majaffar@imamu.edu.sa 1 Department of Computer Science, COMSATS, Institute of Information Technology, Sahiwal, Pakistan 2 School of Information and Mechatronics, Gwangju Institute of Science and Technology, Gwangju, Korea 3 Al Imam Mohammad Ibn Saud Islamic University, Riyadh, Saudi Arabia