Learning-based image representation and method for face recognition
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Extracting multiple features in the CID Color Space for face recognition
IEEE Transactions on Image Processing
New colour SIFT descriptors for image classification with applications to biometrics
International Journal of Biometrics
Color face recognition based on quaternion matrix representation
Pattern Recognition Letters
Color face recognition based on statistically orthogonal analysis of projection transforms
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Iris recognition based on robust iris segmentation and image enhancement
International Journal of Biometrics
Fusing magnitude and phase features for robust face recognition
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Hi-index | 0.00 |
This paper presents a basic color image discriminant (CID) model and its general version for color image recognition. The CID models seek to unify the color image representation and recognition tasks into one framework. The proposed models, therefore, involve two sets of variables: a set of color component combination coefficients for color image representation and one or multiple projection basis vectors for color image discrimination. An iterative basic CID algorithm and its general version are designed to find the optimal solution of the proposed models. The general CID (GCID) algorithm is further extended to generate three color components (such as the three color components of the RGB color images) for further improvement of the recognition performance. Experiments using the face recognition grand challenge (FRGC) database and the biometric experimentation environment (BEE) system show the effectiveness of the proposed models and algorithms. In particular, for the most challenging FRGC version 2 Experiment 4, which contains 12 776 training images, 16 028 controlled target images, and 8014 uncontrolled query images, the proposed method achieves the face verification rate (ROC III) of 78.26% at the false accept rate (FAR) of 0.1%.