Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/10785
Title: Gradient-orientation-based PCA subspace for novel face recognition
Authors: Ghinea, G
Kannan, R
Kannaiyan, S
Keywords: Face recognition;Pattern recognition;Object recognition
Issue Date: 2014
Publisher: Institute of Electrical and Electronics Engineers Inc.
Citation: IEEE Access, 2 : 914 - 920, (2014)
Abstract: Face recognition is an interesting and a challenging problem that has been widely studied in the field of pattern recognition and computer vision. It has many applications such as biometric authentication, video surveillance, and others. In the past decade, several methods for face recognition were proposed. However, these methods suffer from pose and illumination variations. In order to address these problems, this paper proposes a novel methodology to recognize the face images. Since image gradients are invariant to illumination and pose variations, the proposed approach uses gradient orientation to handle these effects. The Schur decomposition is used for matrix decomposition and then Schurvalues and Schurvectors are extracted for subspace projection. We call this subspace projection of face features as Schurfaces, which is numerically stable and have the ability of handling defective matrices. The Hausdorff distance is used with the nearest neighbor classifier to measure the similarity between different faces. Experiments are conducted with Yale face database and ORL face database. The results show that the proposed approach is highly discriminant and achieves a promising accuracy for face recognition than the state-of-the-art approaches.
Description: This article has been made available through the Brunel Open Access Publishing Fund.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6878464
http://bura.brunel.ac.uk/handle/2438/10785
DOI: http://dx.doi.org/10.1109/ACCESS.2014.2348018
ISSN: 2169-3536
Appears in Collections:Brunel OA Publishing Fund
Dept of Computer Science Research Papers

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