NDLI: Facial expression analysis across databases

Content Provider	IEEE Xplore Digital Library
Author	Zheng Zhang Chi Fang Xiaoqing Ding
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Intelligent Technology and Systems, Department of Electronic Engineering, Tsinghua University, Beijing 100084, China (Zheng Zhang; Chi Fang; Xiaoqing Ding)
Abstract	In this paper, we analyze the generalization ability of a facial expression recognition method and also study how to improve it across different databases. Since no common performance metric has been set up, people did experiments in different databases using different methods. So it is difficult for us to judge the performance and the generalization ability of an algorithm. And we also found that the performance is bad in general if we train a facial expression analysis algorithm on a database but test it on another database. So the problem is whether we could improve the generalization ability of a facial expression analysis system and how to improve it more. We extract Gabor features from face images and use SVM to classify the expressions. And we study the above problem using this algorithm on three databases (C-K+, JAFFE and TFEID). We mean to fuse these three databases to solve the problem. And three fusion methods are proposed respectively on the sample level, on the feature level and on the classifier level. Experiments show that the classifiers trained with the fusion methods are of better generalization ability. And the fusion method on the feature level reaches the highest accuracy.
Starting Page	317
Ending Page	320
File Size	363740
Page Count	4
File Format	PDF
ISBN	9781612847719
e-ISBN	9781612847740
DOI	10.1109/ICMT.2011.6001655
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Training Accuracy Databases Face recognition Feature extraction Face Fusion Facial expression Generalization ability
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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