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Neural Computing and Applications

Erschienen in:

01.11.2010 | Original Article

Pose invariant face recognition based on hybrid-global linear regression

verfasst von: Abhishek Sharma, Anamika Dubey, A. N. Jagannatha, R. S. Anand

Erschienen in: Neural Computing and Applications | Ausgabe 8/2010

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Abstract

The paper presents a simple but efficient novel H-eigenface (Hybrid-eigenface) method for pose invariant face recognition ranging from frontal to profile view. H-eigenfaces are entirely new basis for face image representation under different poses and are used for virtual frontal view synthesis. The proposed method is based on the fact that face samples of same person under different poses are similar in terms of the combination pattern of facial features. H-eigenfaces exploit this fact and thus two H-eigenfaces under different poses capture same features of the face. Thereby providing a compact view-based subspace, which can be further used to generate virtual frontal view from inputted non-frontal face image using least square projection technique. The use of proposed methodology on FERET and ORL face database shows an impressive improvement in recognition accuracy and a distinct reduction in online computation when compared to global linear regression method.

Vorheriger Artikel Real-time removal of ocular artifacts from EEG based on independent component analysis and manifold learning

Nächster Artikel The effect of mid-term estimation on back propagation for time series prediction

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Titel: Pose invariant face recognition based on hybrid-global linear regression
verfasst von: Abhishek Sharma
Anamika Dubey
A. N. Jagannatha
R. S. Anand
Publikationsdatum: 01.11.2010
Verlag: Springer-Verlag
Erschienen in: Neural Computing and Applications / Ausgabe 8/2010
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-010-0359-7

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