Conference Papers

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    An E-Learning System with Multifacial Emotion Recognition Using Supervised Machine Learning
    (Institute of Electrical and Electronics Engineers Inc., 2016) Ashwin, T.S.; Jose, J.; Raghu, G.; Guddeti, G.R.
    E-Learning systems based on Affective computingare popularly used for emotional/behavioral analysis of the users. Emotions expressed by the user is depicted by detecting the facialexpression of the user and accordingly the teaching strategies willbe changed. The present eLearning systems mainly focus on thesingle user face detection. Hence, in this paper, we proposemultiuser face detection based eLearning system using supportvector machine based supervised machine learning technique. Experimental results demonstrate that the proposed systemprovides the accuracy of 89% to 100% w.r.t different datasets(LFW, FDDB, and YFD). Further, to improve the speed ofemotional feature processing, we used GPU along with the CPUand thereby achieve a speedup factor of 2. © 2015 IEEE.
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    Unobtrusive students' engagement analysis in computer science laboratory using deep learning techniques
    (Institute of Electrical and Electronics Engineers Inc., 2018) Ashwin, T.S.; Guddeti, R.M.
    Nowadays, analysing the students' engagement using non-verbal cues is very popular and effective. There are several web camera based applications for predicting the students' engagement in an e-learning environment. But there are very limited works on analyzing the students' engagement using the video surveillance cameras in a teaching laboratory. In this paper, we propose a Convolutional Neural Networks based methodology for analysing the students' engagement using video surveillance cameras in a teaching laboratory. The proposed system is tested on five different courses of computer science and information technology with 243 students of NITK Surathkal, Mangalore, India. The experimental results demonstrate that there is a positive correlation between the students' engagement and learning, thus the proposed system outperforms the existing systems. © 2018 IEEE.