Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning

We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the...

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Published in:	Pattern recognition Vol. 47; no. 12; pp. 3738 - 3749
Main Authors:	Deng, Weihong, Hu, Jiani, Zhou, Xiuzhuang, Guo, Jun
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.12.2014 Elsevier
Subjects:	Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Face recognition Feature extraction Generic learning Image processing Information, signal and communications theory Linear regression One sample problem Pattern recognition Signal and communications theory Signal processing Signal, noise Telecommunications and information theory One sample problem Feature extraction Generic learning Face recognition Linear regression Biometrics Performance evaluation State of the art Prototype Image processing Updating Mapping Regression analysis Pattern recognition Algorithm Learning Least squares method Database Signal processing Localization Automatic recognition
ISSN:	0031-3203, 1873-5142
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Abstract	We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or the local structure of the training data, our method, called linear regression analysis (LRA), applies a least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intraclass facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and FERET databases. •Equidistant prototypes embedding that maximizes the minimum one-against-the-rest margin between the classes.•A parameter-free model for the practical usage on one sample problem.•Effective generic learning based on the training samples collected from a different database.•An incremental learning algorithm that makes the model efficiently updated.•Extensive experimental results on the Extended Yale B, CMU PIE, AR, and FERET databases.
AbstractList	We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single gallery sample per subject. We take advantage of the observation that equidistant prototypes embedding is an optimal embedding that maximizes the minimum one-against-the-rest margin between the classes. Rather than preserving the global or the local structure of the training data, our method, called linear regression analysis (LRA), applies a least-square regression technique to map gallery samples to the equally distant locations, regardless of the true structure of training data. Further, a novel generic learning method, which maps the intraclass facial differences of the generic faces to the zero vectors, is incorporated to enhance the generalization capability of LRA. Using this novel method, learning based on only a handful of generic classes can largely improve the face recognition performance, even when the generic data are collected from a different database and camera set-up. The incremental learning based on the Greville algorithm makes the mapping matrix efficiently updated from the newly coming gallery classes, training samples, or generic variations. Although it is fairly simple and parameter-free, LRA, combined with commonly used local descriptors, such as Gabor representation and local binary patterns, outperforms the state-of-the-art methods for several standard experiments on the Extended Yale B, CMU PIE, AR, and FERET databases. •Equidistant prototypes embedding that maximizes the minimum one-against-the-rest margin between the classes.•A parameter-free model for the practical usage on one sample problem.•Effective generic learning based on the training samples collected from a different database.•An incremental learning algorithm that makes the model efficiently updated.•Extensive experimental results on the Extended Yale B, CMU PIE, AR, and FERET databases.
Author	Deng, Weihong Zhou, Xiuzhuang Guo, Jun Hu, Jiani
Author_xml	– sequence: 1 givenname: Weihong surname: Deng fullname: Deng, Weihong organization: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 2 givenname: Jiani surname: Hu fullname: Hu, Jiani organization: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China – sequence: 3 givenname: Xiuzhuang surname: Zhou fullname: Zhou, Xiuzhuang email: xiuzhuangzhou@126.com, zxz@xeehoo.com organization: College of Information Engineering, Capital Normal University, Beijing 100048, China – sequence: 4 givenname: Jun surname: Guo fullname: Guo, Jun organization: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China
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Keywords	One sample problem Feature extraction Generic learning Face recognition Linear regression Biometrics Performance evaluation State of the art Prototype Image processing Updating Mapping Regression analysis Pattern recognition Algorithm Learning Least squares method Database Signal processing Localization Automatic recognition
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Snippet	We develop a parameter-free face recognition algorithm which is insensitive to large variations in lighting, expression, occlusion, and age using a single...
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SubjectTerms	Applied sciences Detection, estimation, filtering, equalization, prediction Exact sciences and technology Face recognition Feature extraction Generic learning Image processing Information, signal and communications theory Linear regression One sample problem Pattern recognition Signal and communications theory Signal processing Signal, noise Telecommunications and information theory
Title	Equidistant prototypes embedding for single sample based face recognition with generic learning and incremental learning
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