NDLI: Two-layer features extracted from essential areas for facial expression recognition

Content Provider	IEEE Xplore Digital Library
Author	Jing-Ming Guo Jain-Long Chen
Copyright Year	2011
Description	Author affiliation: Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan (Jing-Ming Guo; Jain-Long Chen)
Abstract	This work presents a novel two-layer feature extraction method on essential facial areas. First, each original image is divided into three local images (first-layer area) according to the facial organs carried significant information. Second, the local images are down-sampled to images of lower resolutions (second-layer area). Then the texture and frequency features are individually extracted by the Local Binary Pattern (LBP) and Discrete Cosine Transform (DCT) operators. Finally, the Support Vector Machine (SVM) is adopted to classify seven facial expressions with Japanese female facial expression (JAFFE) database. Experimental results shows that the second layer of the proposed scheme can significantly boost the recognition rate by around 10%. As a result, the proposed scheme can obtain the highest recognition rate among various former methods. Moreover, the proposed method can achieve a better recognition rates with a lower resolution than those methods in the literature with higher resolutions.
Starting Page	288
Ending Page	291
File Size	621053
Page Count	4
File Format	PDF
ISBN	9781612843513
e-ISBN	9781612844718
e-ISBN	9781612844725
DOI	10.1109/ICSSE.2011.5961915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Histograms Facial expression recognition Face recognition DCT Feature extraction SVM LBP Discrete cosine transforms Face Pixel
Content Type	Text
Resource Type	Article

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