Multithreading cascade of SURF for facial expression recognition.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	16875176
DOI:	10.1186/s13640-016-0140-7