Face recognition using Extended Curvature Gabor classifier bunch

• We propose extended curvature Gabor kernels as complementary features.
• Homogeneous Classifier Bunch increases accuracy in low/mid-resolution images.
• Parallel boosting method effectively selects salient features from many features.
• We report the best verification rate using the FRGC version 2.0 database.
• We have extensive experimental results.

We describe a novel face recognition using the Extended Curvature Gabor (ECG) Classifier Bunch. First, we extend Gabor kernels into the ECG kernels by adding a spatial curvature term to the kernel and adjusting the width of the Gaussian at the kernel, which leads to numerous feature candidates being extracted from a single image. To handle large feature candidates efficiently, we divide them into multiple ECG coefficients according to different kernel parameters, and then we independently select the salient features from each ECG coefficient using the boosting method. A single ECG classifier is implemented by applying Linear Discriminant Analysis (LDA) to the selected feature vector. To overcome the accuracy limitation of a single classifier, we propose an ECG classifier bunch that combines multiple ECG classifiers with the fusion scheme. We confirm the generality of the performances of the proposed method using the FRGC version 2.0, XM2VTS, BANCA, and PIE databases.