NDLI: A Fast Learning Algorithm for Robotic Emotion Recognition

Content Provider	IEEE Xplore Digital Library
Author	Jung-Wei Hong Meng-Ju Han Kai-Tai Song Fuh-Yu Chang
Copyright Year	2007
Description	Author affiliation: Nat. Chiao Tung Univ., Hsinchu (Jung-Wei Hong; Meng-Ju Han; Kai-Tai Song)
Abstract	The capability of robotic emotion recognition is an important factor for human-robot interaction. In order to facilitate a robot to function in daily live environments, a emotion recognition system needs to accommodate itself to various persons. In this paper, an emotion recognition system that can adapt to new facial data is proposed. The main idea of the proposed learning algorithm is to adjust parameters of SVM hyperplane for learning emotional expressions of a new face. After mapping the input space to Gaussian-kernel space, support vector pursuit learning (SVPL) is applied to retrain the hyperplane in the new feature space. To expedite the retraining procedure, only samples classified incorrectly in previous iteration are combined with critical historical sets to restrain a new SVM classifier. After adjusting hyperplane parameters, the new classifier will recognize previous erroneous facial data. Experimental results show that the proposed system recognize new facial data with high correction rates after fast retraining the hyperplane. Moreover, the proposed method also keeps satisfactory recognition rate of old facial samples.
Starting Page	25
Ending Page	30
File Size	510718
Page Count	6
File Format	PDF
ISBN	1424407893
DOI	10.1109/CIRA.2007.382865
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Emotion recognition Support vector machines Support vector machine classification Face recognition Robotics and automation Human robot interaction Intelligent robots Control engineering Training data Computational intelligence
Content Type	Text
Resource Type	Article

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