Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Active Appearance Model

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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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    Early diagnosis of osteoporosis using active appearance model and metacarpal radiogrammetry
    (Institute of Electrical and Electronics Engineers Inc., 2017) Sam, M.; Areeckal, A.S.; Sumam David, S.
    Osteoporosis is a condition of fragile bone with an increased susceptibility to fracture. Since the gold standard method used for the diagnosis of osteoporosis, Dual X-ray Absorptiometry (DXA), is expensive and not widely available in low economies, there is a need for low cost approaches to detect bone loss in people. A new automated radiogrammetric method for early diagnosis of osteoporosis from a single hand radiograph is proposed. In this technique, the third metacarpal bone is segmented from hand X-ray images using Active Appearance Models (AAM). Points of interest acquired from the segmented bone are used to take radiogrammetric measurements, from which bone indices are calculated. Data used in this work was acquired from 138 subjects in two hospitals in India. Significant radiogrammetric features were selected using statistical analysis. The bone indices are observed to be significantly correlated with Bone Mineral Density (BMD) of the lumbar spine measured using DXA. Different classification models were trained using the significant features. The results obtained are promising and can be used as a cost effective diagnostic tool for early detection of osteoporosis. © 2017 IEEE.