NDLI: Development of advanced Active Appearance Model for facial emotion recognition

Content Provider	IEEE Xplore Digital Library
Author	Kwang-Eun Ko Kwee-Bo Sim
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Electronic Engineering, Chung-Ang University, Korea (Kwang-Eun Ko; Kwee-Bo Sim)
Abstract	We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.
Starting Page	1019
Ending Page	1022
File Size	1196527
Page Count	4
File Format	PDF
ISBN	9781424443475
DOI	10.1109/ISIE.2009.5213203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-07-05
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Active appearance model Emotion recognition Face recognition Image reconstruction Humans Image recognition Facial features Bayesian methods Eyes Mouth
Content Type	Text
Resource Type	Article

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