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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2019 Vol.9(1): 14-19 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.1.759

Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks

Wisal Hashim Abdulsalam, Rafah Shihab Alhamdani, and Mohammed Najm Abdullah
Abstract—Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

Index Terms—Facial emotion recognition, deep convolutional neural network, TensorFlow, ADFES-BIV, WSEFEP.

Wisal Hashim Abdulsalam and Rafah Shihab Alhamdani are with Iraqi Commission for Computers & Informatics, Baghdad, Iraq (e-mail: wisal.h@ihcoedu.uobaghdad.edu.iq).
Mohammed Najm Abdullah is with the Department of Computer Engineering, University of Technology, Baghdad, Iraq.

[PDF]

Cite: Wisal Hashim Abdulsalam, Rafah Shihab Alhamdani, and Mohammed Najm Abdullah, "Facial Emotion Recognition from Videos Using Deep Convolutional Neural Networks," International Journal of Machine Learning and Computing vol. 9, no. 1, pp. 14-19, 2019.

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