Laughter emotion recognition using gestures
Date of Publication
Bachelor of Science in Computer Science
Programming Languages and Compilers
College of Computer Studies
Defense Panel Chair
Merlin Teodosia Suarez
Defense Panel Member
Laughter is a form of communicative response by humans, and there are many forms of laughter as well as gestures indicating what kind of laughter however there are few studies concentrating on it. With the use of the body-tracking technology in Microsoft Kinect, it is possible to detect the movements of the subject while they are laughing.
These points are then taken and computed into features such as head tilt, shift in seat, leaning, shoulder shift center, shoulder shift right and shoulder shift left. Then, this data is fed into Weka using three algorithms namely C.45, kNN and SVM with Polykernel, Puk and RBF kernel and tested using 10-fold cross validation with combined, individual, adjusted and male/female datasets. SVM was classified with its own adjusted dataset.
The highest correctly classified instances between kNN and C4.5 was at 95.5% with kappa statistic of 0.9435 and RMSE of 0.1348, from Natutuwa adjusted instances dataset. With SVM, it is SVM with Puk kernel that performed the best, with 77.0% correctly classified instances, kappa statistic of 0.5405 and an RMSE 0.4793 from the comparison of nasasabik.
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De Jesus, P. (2014). Laughter emotion recognition using gestures. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/2654