Conference Papers

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    An approach to maintain attendance using image processing techniques
    (Institute of Electrical and Electronics Engineers Inc., 2017) Yuvaraj, C.B.; Madikeri, M.; Santhosh Kumar, V.; Vishnu Srinivasa Murthy, Y.V.; Koolagudi, S.G.
    Nowadays, the research is growing towards the invention of new approaches. One such most attracted application is face recognition of image processing. There are several innovative technologies have been developed to take attendance. Some prominent ones are biometric, thumb impressions, access card, and fingerprints. The method proposed in this paper is to record the attendance through image using face detection and face recognition. The proposed approach has been implemented in four steps such as face detection, labelling the detected faces, training a classifier based on labelled dataset, and face recognition. The database has been constructed with the positive images and negative images. The complete database has been divided into training and testing set and further, processed by a classifier to recognize the faces in a classroom. The final step is to take the attendance using face recognition technique in which the input image of a classroom is given, and faces of the given image will be detected along with their IDs. The frames of a video taken for a minute is taken into consideration to avoid the missed ones due to rotational issues. © 2017 IEEE.
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    CVUCAMS: Computer vision based unobtrusive classroom attendance management system
    (Institute of Electrical and Electronics Engineers Inc., 2018) Gupta, S.K.; Ashwin, T.S.; Guddeti, R.M.
    One of the major challenges in a smart classroom environment is to develop a computer vision based unobtrusive classroom attendance management system. Traditional classroom environment follows a manual attendance marking system either by calling the student's names or by forwarding an attendance sheet; both interrupts the teaching-learning process and also consume a lot of time. Further, it can be erroneous due to factors such as students' proxy etc. In this paper, we propose an unobtrusive face recognition based smart classroom attendance management system using the high definition rotating camera for capturing the faces of students. The proposed system uses Max-Margin Face Detection (MMFD) technique for the face detection and the model is trained using the Inception-V3 CNN technique for the students' identification. The proposed smart classroom system was tested for a classroom with 20 students at National Institute of Technology Karnataka Surathkal, Mangalore, India and we got the experimental results demonstrate the train and test accuracy of 97.67% and 96.66%, respectively. © 2018 IEEE.
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    An IoT-Enabled Stress Detection Scheme Using Facial Expression
    (Institute of Electrical and Electronics Engineers Inc., 2022) Angalakuditi, A.; Bhowmik, B.R.
    Depression is a significant problem in our society, as it is the cause of many health problems. The ongoing burden of intellectual function and continuous technological development, leading to constant change and the need for flexibility, makes the situation even more significant for people. It is necessary to see it early to prevent stress from becoming chronic and irreversible irritability. Unfortunately, a way to detect automatic, continuous, invisible pressure does not exist. This work involves monitoring a person's attention and emotional state across the ages. An IoT-enabled unobtrusive real-time monitoring system is developed to detect the person's emotional states by analyzing facial expression videos. The proposed method identifies individual emotions in each video frame, and a decision on the level of stress is made at the sequence level. © 2022 IEEE.