A novel feature extraction method for face recognition under different lighting conditions
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Theories and applications of LBP: a survey
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
Fast multi-scale local phase quantization histogram for face recognition
Pattern Recognition Letters
Face recognition based on combination of human perception and local binary pattern
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Force work induced metric for face verification
IScIDE'12 Proceedings of the third Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Semantic pixel sets based local binary patterns for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Learning discriminant face descriptor for face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Face recognition with enhanced local directional patterns
Neurocomputing
Face recognition using Weber local descriptors
Neurocomputing
Face recognition using scale-adaptive directional and textural features
Pattern Recognition
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Information jointly contained in image space, scale and orientation domains can provide rich important clues not seen in either individual of these domains. The position, spatial frequency and orientation selectivity properties are believed to have an important role in visual perception. This paper proposes a novel face representation and recognition approach by exploring information jointly in image space, scale and orientation domains. Specifically, the face image is first decomposed into different scale and orientation responses by convolving multiscale and multi-orientation Gabor filters. Second, local binary pattern analysis is used to describe the neighboring relationship not only in image space, but also in different scale and orientation responses. This way, information from different domains is explored to give a good face representation for recognition. Discriminant classification is then performed based upon weighted histogram intersection or conditional mutual information with linear discriminant analysis techniques. Extensive experimental results on FERET, AR, and FRGC ver 2.0 databases show the significant advantages of the proposed method over the existing ones.