Face Recognition Using the Discrete Cosine Transform
International Journal of Computer Vision - Special issue: Research at McGill University
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Texture-Based Method for Modeling the Background and Detecting Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition Letters
Description of interest regions with local binary patterns
Pattern Recognition
Periocular biometrics in the visible spectrum: a feasibility study
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
On the Fusion of Periocular and Iris Biometrics in Non-ideal Imagery
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Description of interest regions with center-symmetric local binary patterns
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
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A novel method by fusing the features of discrete cosine transform (DCT) and multi-level center-symmetric local binary pattern (CS-LBP) is proposed for periocular recognition in this paper. Because that CS-LBP is used to extract features from the images only once, by which the extracted texture features are not adequate to represent the periocular images, we employ multi-level CS-LBP to extract more abundant and informative texture features for more times to get the spatial features. The primary information of the periocular image was centralized in a small number of DCT coefficients which were used as the frequency features of the image. The periocular image was divided regularly into small regions from which histograms were computed and concatenated into a spatial global histogram used as descriptor vector of the periocular image. Then the DCT features and the CS-LBP features were fused posterior to the normalization. Experimental results on ORL face database, AR face database and Morph periocular database demonstrate that the proposed method outperforms DCT or LBP features for periocular recognition.