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Multithreading cascade of SURF for facial expression recognition.

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Title: Multithreading cascade of SURF for facial expression recognition.
Authors: Chen, Jinhui1 ianchen@me.cs.scitec.kobe-u.ac.jp, Luo, Zhaojie2 luozhaojie@me.cs.scitec.kobe-u.ac.jp, Takiguchi, Tetsuya3 takigu@kobe-u.ac.jp, Ariki, Yasuo3 ariki@kobe-u.ac.jp
Source: EURASIP Journal on Image & Video Processing. 10/22/2016, Vol. 2016 Issue 1, p1-13. 13p.
Subjects: Simultaneous multithreading processors, Facial expression, Theory of distributions (Functional analysis), Receiver operating characteristic curves, Data analysis
Abstract: We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features (McSURF), which is capable of processing multiple classifications simultaneously and accurately. The proposed framework adopts SURF features, but the framework is a multi-class and simultaneous cascade, i.e., a multithreading cascade. McSURF is implemented by configuring an area under the receiver operating characteristic (ROC) curve (AUC) of the weak SURF classifier for each data category into a real-value lookup list. These non-interfering lists are built into thread channels to train the boosting cascade for each data category. This boosting cascade-based approach can be trained to fit complex distributions and can simultaneously and robustly process multi-class events. The proposed method takes facial expression recognition as a test case and validates its use on three popular and representative public databases: the Extended Cohn-Kanade, MMI Facial Expression Database, and Annotated Facial Landmarks in the Wild database. Overall results show that this framework outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="AR" term="%22Chen%2C+Jinhui%22">Chen, Jinhui</searchLink><relatesTo>1</relatesTo><i> ianchen@me.cs.scitec.kobe-u.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Luo%2C+Zhaojie%22">Luo, Zhaojie</searchLink><relatesTo>2</relatesTo><i> luozhaojie@me.cs.scitec.kobe-u.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Takiguchi%2C+Tetsuya%22">Takiguchi, Tetsuya</searchLink><relatesTo>3</relatesTo><i> takigu@kobe-u.ac.jp</i><br /><searchLink fieldCode="AR" term="%22Ariki%2C+Yasuo%22">Ariki, Yasuo</searchLink><relatesTo>3</relatesTo><i> ariki@kobe-u.ac.jp</i>
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  Data: <searchLink fieldCode="JN" term="%22EURASIP+Journal+on+Image+%26+Video+Processing%22">EURASIP Journal on Image & Video Processing</searchLink>. 10/22/2016, Vol. 2016 Issue 1, p1-13. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Simultaneous+multithreading+processors%22">Simultaneous multithreading processors</searchLink><br /><searchLink fieldCode="DE" term="%22Facial+expression%22">Facial expression</searchLink><br /><searchLink fieldCode="DE" term="%22Theory+of+distributions+%28Functional+analysis%29%22">Theory of distributions (Functional analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Receiver+operating+characteristic+curves%22">Receiver operating characteristic curves</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink>
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  Data: We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features (McSURF), which is capable of processing multiple classifications simultaneously and accurately. The proposed framework adopts SURF features, but the framework is a multi-class and simultaneous cascade, i.e., a multithreading cascade. McSURF is implemented by configuring an area under the receiver operating characteristic (ROC) curve (AUC) of the weak SURF classifier for each data category into a real-value lookup list. These non-interfering lists are built into thread channels to train the boosting cascade for each data category. This boosting cascade-based approach can be trained to fit complex distributions and can simultaneously and robustly process multi-class events. The proposed method takes facial expression recognition as a test case and validates its use on three popular and representative public databases: the Extended Cohn-Kanade, MMI Facial Expression Database, and Annotated Facial Landmarks in the Wild database. Overall results show that this framework outperforms other state-of-the-art methods. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of EURASIP Journal on Image & Video Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 10/22/2016
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