Enhancing CNN with Preprocessing Stage in Automatic Emotion Recognition

Diah Anggraeni Pitaloka, Ajeng Wulandari, T. Basaruddin, Dewi Yanti Liliana

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

44 Citations (Scopus)

Abstract

Emotion recognition from facial expression is the subfield of social signal processing which is applied in wide variety of areas, specifically for human and computer interaction. Many researches have been proposed for automatic emotion recognition, which is fundamentally using machine learning approach. However, recognizing basic emotions such as angry, happy, disgust, fear, sad, and surprise is still becoming a challenging problem in computer vision. Lately, deep learning has gained more attention to solve many real-world problems, including emotion recognition. In this research, we enhanced Convolutional Neural Network method to recognize 6 basic emotions and compared some preprocessing methods to show the influences of its in CNN performance. The compared data preprocessing methods are: resizing, face detection, cropping, adding noises, and data normalization consists of local normalization, global contrast normalization and histogram equalization. Face detection as single pre-processing phase achieved significant result with 86.08 % of accuracy, compared with another pre-processing phase and raw data. However, by combining those techniques can boost performance of CNN and achieved 97.06% of accuracy.

Original languageEnglish
Title of host publication2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017
EditorsWdodo Budiharto, Dewi Suryani, Lili A. Wulandhari, Andry Chowanda, Alexander A.S. Gunawan, Novita Hanafiah, Hanry Ham, Meiliana
PublisherElsevier B.V.
Pages523-529
Number of pages7
ISBN (Print)9781510849914
DOIs
Publication statusPublished - 1 Jan 2017
Event2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017 - Bali, Indonesia
Duration: 13 Oct 201714 Oct 2017

Publication series

NameProcedia Computer Science
Volume116
ISSN (Electronic)1877-0509

Conference

Conference2nd International Conference on Computer Science and Computational Intelligence, ICCSCI 2017
Country/TerritoryIndonesia
CityBali
Period13/10/1714/10/17

Keywords

  • Computer vision
  • Convolutional neural network
  • Emotion recognition
  • Facial expression
  • Machine learning
  • Normalization

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