NDLI: A framework of human emotion recognition using extreme learning machine

Content Provider	IEEE Xplore Digital Library
Author	Utama, P. Widodo Ajie, H.
Copyright Year	2014
Description	Author affiliation: Univ. Negeri Jakarta, Jakarta, Indonesia (Utama, P.; Widodo; Ajie, H.)
Abstract	Human emotion recognition has been challenging issue in field of human-computer interaction. In order to form an interaction that is more natural between human and com-puter, the computer should be able to discern and respond to human emotion. In this paper, an approach for recognizing human emotion is proposed. The proposed approach operates HAAR-classifier to detect mouth, eyes, and eyebrow on face, and, to extract features from them, it uses Gabor wavelet. Before classifying the features, PCA is performed to reduce its dimension. The proposed framework employs SLFNs with ELM as its learning algorithm to classify the features. In this experimental, the proposed framework is tested in two cases, personalize and generalize face case, with ten subjects expressing six basic emotions and neural state. The robustness of ELM is evaluated with comparing it to K-NN and SVM. Preliminary experiment shows that the proposed approach has promising performance in personalize face case.
Starting Page	315
Ending Page	320
File Size	195850
Page Count	6
File Format	PDF
ISBN	9781479969845
e-ISBN	9781479951000
DOI	10.1109/ICAICTA.2014.7005961
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-20
Publisher Place	Indonesia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Emotion recognition Gabor Wavelet Extreme Learning Machine Image edge detection Mouth SLFNs Human Emotion Recognition Feature extraction Face Human Computer Interaction
Content Type	Text
Resource Type	Article

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