Faculty of Computing

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Author: search.filters.author.Samarasinghe, PSubject: search.filters.subject.Learning Based

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    Machine Learning Based Emotion Level Assessment
    (IEEE, 2021-12-09) Wickremesinghe, L; Madanayake, D; Karunasena, A; Samarasinghe, P
    With recent advancements of technology, identification of emotions of humans via facial recognition is done with the application of numerous methods including machine learning and deep learning. In this paper, machine learning techniques are applied for identifying different levels of emotions of individuals for unannotated video clips using Facial Action Coding System. In order to archive the above, first, two methods were experimented to obtain a labeled image data set to train classification models where in the first method, clustering of images was done using Action Units(AU) identified from literature and the emotion levels of the images were determined through the resulted clusters and images are labeled according to the cluster they belonged to. In the second method, the image set is analyzed explicitly to identify AUs contributing to emotions rather than relying on those identified in literature and then the clustering of image set was done using those identified AUs to label the images similar to the first method. The two labeled data sets were used to train classification models with Random Forest, Support Vector Machine and K-Nearest Neighbour algorithms separately.Classification models showed better accuracy with data set produced using the second method. An overall F1 score, accuracy, precision and recall of 87% was obtained for the best classification model which is developed using the Random Forest algorithm to identify levels of emotions. Identifying the AU combinations related to emotions and developing a classification model for identifying levels of emotions are the major contributions of this paper. The results of this research would be especially useful to identify levels of emotions of individuals who are having issues in verbal communication.
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    Deep Learning Based Dog Behavioural Monitoring System
    (IEEE, 2020-12-03) Boteju, W. J. M; Herath, H. M. K. S; Peiris, M. D. P; Wathsala, A. K. P. E; Samarasinghe, P; Weerasinghe, L
    Dogs are one of the most popular pets in the world. It is usual that pet owners are always concerned about the health and the wellbeing of their pets. The activity levels of the dogs vary from each other based on breed and age. Tracking the behavioral changes using image processing and machine learning concepts and notifying the pet owners via a mobile application is the main objective of this research. Breed recognition has been done applying deep learning concepts to the user-uploaded video or the photograph of the dog. This research mainly focuses on walking, running, resting, and barking activity patterns of the dog. A surveillance camera and sensors were the main equipment for data collection. The audio feature of the surveillance camera is used to identity the barking behavior of the dog. Dogs from different ages belonging to Pomeranian and German Shepherd breeds have been selected for this experiment. Transfer learning with ResNet50, Inception V3, and support vector machines have been used to recognize and classify the activities of the dogs. The research study was able to achieve the accuracy levels as follows: - breed recognition - 89%+, walking pattern recognition - 99.5%, resting pattern recognition - 97% and barking pattern recognition - 60%. With the above accuracy levels, the research was able to identify the unusual behaviour of the dogs.