Emotion classification using hidden layer outputs

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2012

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Airframe and Powerplant Maintenance
(2012)
The Atılım University Department of Airframe and Powerplant Maintenance has been offering Civil Aviation education in English since 2012. In an effort to provide the best level of education, ATILIM UNIVERSITY demonstrated its merit as a role model in Civil Aviation Education last year by being granted a SHY 147 certificate with the status of “Approved Aircraft Maintenance Training Institution” by the General Directorate of Civil Aviation. The SHY 147 is a certificate for Approved Aircraft Maintenance Training Institutions. It is granted to institutions where training programs have undergone inspection, and the quality of the education offered has been approved by the General Directorate of Civil Aviation. With our Civil Aviation Training Center at Esenboğa Airport (our hangar), and the two Cessna-337 planes with double piston engines both of which are fully operational, as well our Beechcraft C90 Kingait plaine with double Turboprop engines, Atılım University is an institution to offer hands-on technical training in civil aviation, and one that strives to take the education it offers to the extremes in terms of technology. The Atılım university Graduate School Department of Airframe and Powerplant Maintenance is a fully-equipped civil aviation school to complement its theoretical education with hands-on training using planes of various kinds. Even before their graduation, most of our students are hired in Turkey’s most prestigious institutions in such a rapidly-developing sector. We are looking forward to welcoming you at this modern and contemporary institution for your education in civil aviation.

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Abstract

Neural network (NN) with Multi-Layer Perceptron (MLP) is a supervised learning algorithm composed of artificial neurons. Multilayer NN is capable of solving nonlinear classification problems such as emotion identification by using facial expressions that is presented in this paper. Hidden layer outputs of NN provide useful information about facial appearance. This study addresses that without fully training NN hidden layer outputs can be used as feature. It is shown that an acceptable recognition rate is obtained by means of hidden layer outputs. © 2012 IEEE.

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facial expressions, image processing, Multi layer neural network

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3

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INISTA 2012 - International Symposium on INnovations in Intelligent SysTems and Applications -- International Symposium on INnovations in Intelligent SysTems and Applications, INISTA 2012 -- 2 July 2012 through 4 July 2012 -- Trabzon -- 92831

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https://doi.org/10.1109/INISTA.2012.6247027
 https://hdl.handle.net/20.500.14411/3668

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