Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition
Computer Vision and Image Understanding
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
Face recognition using new image representations
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Learning-based image representation and method for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Emulating biological strategies for uncontrolled face recognition
Pattern Recognition
Fusing local patterns of gabor magnitude and phase for face recognition
IEEE Transactions on Image Processing
Kernel fusion of multiple histogram descriptors for robust face recognition
SSPR&SPR'10 Proceedings of the 2010 joint IAPR international conference on Structural, syntactic, and statistical pattern recognition
Local Kernel Feature Analysis (LKFA) for object recognition
Neurocomputing
Fast multi-scale local phase quantization histogram for face recognition
Pattern Recognition Letters
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Fusing magnitude and phase features for robust face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
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The face recognition system based on the only single classifier considering the restricted information can not guarantee the generality and superiority of performances in a real situation. To challenge such problems, we propose the hybrid Fourier features extracted from different frequency bands and multiple face models. The hybrid Fourier feature comprises three different Fourier domains; merged real and imaginary components, Fourier spectrum and phase angle. When deriving Fourier features from three Fourier domains, we define three different frequency bandwidths, so that additional complementary features can be obtained. After this, they are individually classified by Linear Discriminant Analysis. This approach makes possible analyzing a face image from the various viewpoints to recognize identities. Moreover, we propose multiple face models based on different eye positions with a same image size, and it contributes to increasing the performance of the proposed system. We evaluated this proposed system using the Face Recognition Grand Challenge (FRGC) experimental protocols known as the largest data sets available. Experimental results on FRGC version 2.0 data sets has proven that the proposed method shows better verification rates than the baseline of FRGC on 2D frontal face images under various situations such as illumination changes, expression changes, and time elapses.