Supervised Locally Linear Embedding in face recognition


Ying Han, Pang and Andrew Beng Jin, Teoh and Eng Kiong, Wong and Fazly Salleh, Abas (2008) Supervised Locally Linear Embedding in face recognition. In: International Symposium on Biometrics and Security Technologies, 23-24 APR 2008, Islamabad, PAKISTAN .

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Locally Linear Embedding (LLE), which has recently emerged as a powerful face feature descriptor, suffers from a limitation. That is class-specific information of data is lacked of during face analysis. Thus, we propose a supervised LLE technique, known as class-label Locally Linear Embedding (cLLE), to overcome the problem. cLLE is able to discover the nonlinearity of high-dimensional face data by minimizing the global reconstruction error of the set of all local neighbors in the data set. cLLE utilizes user class-specific information in neighborhoods selection and thus preserves the local neighborhoods. Since the locality preservation is correlated to the class discrimination, the proposed cLLE is expected superior to LLE in face recognition. Experimental results on three face databases: ORL, AR and Yale databases, demonstrate that the proposed technique obtains better recognition performance than PCA and LLE.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 21 Sep 2011 07:32
Last Modified: 21 Sep 2011 07:32


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