Browsing by Author "Samarasinghe, P."

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Facial Emotion Prediction through Action Units and Deep Learning
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Nadeeshani, M.; Jayaweera, A.; Samarasinghe, P.
With the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.
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Individualized Edutainment and Parent Supportive Tool for ADHD Children
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Thennakoon, A.; Perera, D.; Sugathapala, S.; Weerasingha, S.; Samarasinghe, P.; Dahanayake, D.; Piyawardan, V.S.
Attention-Deficit/Hyperactivity Disorder (ADHD) is a comorbid disorder that can impact a child and his/her family. ADHD children have considerable obstacles in managing time, understanding instructions, and paying attention to the activities. To address these perplexities, this research has designed a mobile application to help parents to have better interaction with the children and for the children to enjoy their learning activities. The specialty of this application is the models are trained on individual child skills and needs. Issues with time management are handled by the Scheduler component while the Instruction Predictor module supports the parent in recognizing the child's understandability level. Furthermore, the children are provided with edutainment activities based on their attention and ability levels. Different models have been used in predicting the results through these modules and the prediction result accuracy exceeds 90% in most of the cases. Out of the many models, The Random Forest model resulted in the best overall performance. The application was tried by many parents and health professionals and received satisfactory and commendable reviews.
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OMNISCIENT: A Branch Monitoring System for Large-scale Organizations
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayasekara, T.; Omalka, K.; Hewawelengoda, P.; Kanishka, C.; Samarasinghe, P.; Weerasinghe, L.
Omniscient is a system that enables higher-level management of massive organizations to remotely monitor and scrutinize the activities that take place in the branches from the head office itself by providing exclusive insight in the form of detailed reports on the employees’ behaviour and performance daily, weekly and monthly. The system further monitors the branch and provides reports on any suspicious behaviour and also on the customers’ activity within the branch premises. Omniscient rates the customer’s level of satisfaction by capturing the customer’s facial expressions and analyzing their emotions while they are being served. The employee face and dress recognition models have accuracies of 90.90% and 87.00% respectively while, employee activity detection has an accuracy of 89.00%. Customer emotion and miscellaneous activities detection models have the accuracies of 91.50% and 83.00% respectively. All of the aforementioned procedures were made possible by systematically analyzing the IP camera video footage obtained throughout the day to analyze the work productivity and performance of the branch as accurately as possible using deep learning and modern visual computing techniques like CNN, OpenCV, Haar Cascade classifier, face recognition, Dlib and Darknet.