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Author: search.filters.author.Samarasinghe, PSubject: search.filters.subject.Automated

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    The Automated Temporal Analysis of Gaze Following in a Visual Tracking Task
    (Springer, Cham, 2022-05-15) Dhanawansa, V; Samarasinghe, P; Gardiner, B; Yogarajah, P; Karunasena, A
    The attention assessment of an individual in following the motion of a target object provides valuable insights into understanding one’s behavioural patterns in cognitive disorders including Autism Spectrum Disorder (ASD). Existing frameworks often require dedicated devices for gaze capture, focus on stationary target objects, or fails to conduct a temporal analysis of the participant’s response. Thus, in order to address the persisting research gap in the analysis of video capture of a visual tracking task, this paper proposes a novel framework to analyse the temporal relationship between the 3D head pose angles and object displacement, and demonstrates its validity via application on the EYEDIAP video dataset. The conducted multivariate time-series analysis is two-fold; the statistical correlation computes the similarity between the time series as an overall measure of attention; and the Dynamic Time Warping (DTW) algorithm aligns the two sequences, and computes relevant temporal metrics. The temporal features of latency and maximum time of focus retention enabled an intragroup comparison between the performance of the participants. Further analysis disclosed valuable insights into the behavioural response of participants, including the superior response to horizontal motion of the target and the improvement in retention of focus on the vertical motion over time, implying that following a vertical target initially proved a challenging task.
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    CIS: an automated criminal identification system
    (IEEE, 2018-12-21) Rasanayagam, K; Kumarasiri, S. D. D. C; Tharuka, W. A. D. D; Samaranayake, N. T; Samarasinghe, P; Siriwardana, S. E. R
    The identification of criminals and terrorists is a primary task for police, military and security forces. The terrorist activities and crime rate had increased abnormally. Combating them is a challenging task for all security departments. Presently, these departments are using latest technologies. But they have not enough efficient and accuracy as they expected This research study is based on the analysis of faces, emotions, Ages and genders to identify the suspects. Face recognition, emotion, age and gender identifications are implemented using deep learning based CNN approaches. Suits identification is based on LeNet architecture. In the implementation phase for the classification purpose, Keras deep learning library is used, which is implemented on top of Tensorflow. IMDb is the dataset used for the whole training purpose. Training is performed using in AWS cloud which is more powerful and capable way of training instead of using local machines. Real-time Video and images are taken for the experiment. Results of the training and predictions are discussed below in brief.