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Facial Features Based Hybrid Methods for Emotion Recognition
Published in Blue Eyes Intelligence Engineering& Sciences Publication
Volume: 8.0
Issue: S210
Pages: 385.0 - 389.0
Effective human machine interaction systems areneed of the time so the work carried out deals with one of suchsignificant HMI tasks- automatic emotion recognition. Theexperimentation carried out for this study is focused to facialexpressions based emotion recognition. Two techniques ofemotion recognition based on hybrid features are designed andexperimented using JAFFE database. The first techniquereferred as "Hybrid Method1" is designed around featuredescriptor obtained through local directional number &principal component analysis and feed forward neural networkused as classifier. The second technique referred as "HybridMethod 2" is designed around feature descriptor obtainedthrough histogram of oriented gradients, local binary patternand Gabor filters. PCA- principal component analysis is used fordimensionality reduction of feature descriptor and k-nearestneighbors as classifier. The average emotion recognitionaccuracy achieved through method 1 and method 2 is 85.24%and 93.86% respectively. Effectiveness of both the techniques iscompared on the basis of performance parameters such asaccuracy, false positive rate, false negative rate and emotionrecognition time. Emotion recognition has wide applicationareas so the work carried out can be applied for suitableapplication development.
About the journal
JournalInternational Journal of Recent Technology and Engineering (IJRTE)
PublisherBlue Eyes Intelligence Engineering& Sciences Publication
Open AccessNo