Symposia & Conferences

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Creating a Sri Lankan Micro-Emotion Dataset for a Robust Micro-Expression Recognition System
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayakodi, J. A. L. P.; Jayamali, G. G. S. D.; Hirshan, R.; Aashiq, M. N. M.; Kumara, W. G. C. W.
In interpersonal communication, the human face provides important signals of a person’s emotional states and intentions. Furthermore, micro-emotions play a major role in understanding hidden intentions. In psychological aspects, detecting micro-emotions play a major role. In addition, lie detection, criminal identification, and security systems are other applications, where detecting micro-emotion accurately is essential. Revealing a micro-expression is quite difficult for humans because people tend to conceal their subtle emotions. As a result, training a human is expensive and time-consuming. Therefore, it is important to develop robust computer vision and machine learning methods to detect micro-emotions. Convolutional Neural Network (CNN) is the most used deep learning-based method in recent years. This research focuses on developing a 3D-CNN model to detect and classify Micro-emotions and creating a local Micro-emotion database. From the related research work we have considered this is the first attempt made at creating a Sri Lankan micro-emotion dataset. Having a local micro-emotion dataset is essential in formulating more accurate real-time applications focused on deep learning methods. Therefore, in this research, our main objective is to create a Sri Lankan micro-emotion database for future micro-emotion recognition and detection research.
Mapping of Sri Lankan Road Signs by Using Google Street View Images
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Kiridana, Y. M. W. H. M. R. P. J. R. B.; Weerarathna, P. L. M.; Wijesingha, W. P. D. Y.; Aashiq, M. N. M.; Kumara, W. G. C. W.; Haleem, M. A. L. A.
The development of autonomous vehicle driving systems and Intelligent Transportation System (ITS) have drawn massive attention since the 1980s. For the development of ITS, road sign detection and identification are considered to be very important due to the vital information provided by road signs. Generally, real-time video-based methods are used as the source of images for the operation of ITS. But they are inefficient and costly due to certain limitations like weather conditions, lighting conditions, and limited range in obtaining quality images. To overcome the limitations of the video-based approach, this research aims to develop techniques for detecting and identifying road signs by using Google Street View (GSV) as the image source, OpenCV for image processing and CNN for road sign identification. EdleNet, LeNet-5, and DenseNet were identified as accurate CNN models. Using images from GSV, generating a database of road signs with the relevant coordinates was possible, which is currently unavailable in Sri Lanka. In addition, this process leads to the generation of a valuable image dataset of Sri Lankan road sign images, and a web interface with mapped road signs. Consequently, this research would yield useful findings that may be applied to future research and provide the means to develop ITS, accident-avoidance systems, and driver assistance systems.