Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/100615
Title: Feature extraction through binary pattern of phase congruency for facial expression recognition
Authors: Shojaeilangari, Seyedehsamaneh
Yau, Wei-Yun
Li, Jun
Teoh, Eam Khwang
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2012
Source: Shojaeilangari, S., Yau, W. Y., Li, J., & Teoh, E. K. (2012). Feature extraction through binary pattern of phase congruency for facial expression recognition. 12th International Conference on Control Automation Robotics & Vision (ICARCV), 166-170.
Abstract: Although facial expression plays an important role in human interaction, automated facial expression analysis is still a challenging task. This paper presents a novel facial descriptor based on Phase Congruency (PC) and Local Binary Pattern (LBP) for facial expression recognition. The proposed descriptor, named Binary Pattern of Phase Congruency (BPPC), is an oriented and multi-scale local descriptor that is able to encode various patterns of face images. It is constructed by applying LBP on the oriented PC images. We evaluated the proposed method using the Cohn-Kanade (CK+) database. In our experiment, we achieved an overall detection rate of 93.83% for the six basic emotions. This shows the effectiveness of the proposed method.
URI: https://hdl.handle.net/10356/100615
http://hdl.handle.net/10220/18012
DOI: http://dx.doi.org/10.1109/ICARCV.2012.6485152
Rights: © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICARCV.2012.6485152].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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