Face recognition system using Extended Curvature Gabor classifier bunch for low-resolution face image

We describe a face recognition framework based on Extended Curvature Gabor (ECG) Classifier Bunch. First we extend Gabor wavelet kernels into the ECG wavelet kernels by adding a spatial curvature term to the kernel and adjusting the width of the Gaussian at the kernel, which leads to extraction of numerous feature candidates from a low-resolution image. To handle them efficiently, we divide a pool of feature candidates into plural ECG wavelet coefficient sets according to different kernel parameters. For each ECG wavelet coefficient set, the boosting learning scheme selects significant features. A single ECG classifier is implemented by applying Linear Discriminant Analysis (LDA) to the selected feature, and then, to overcome the accuracy limitation of a single classifier, we propose the ECG classifier bunch that combines plural ECG classifiers by means of the proposed log-likelihood ratio-based score fusion scheme. For evaluations of the proposed method, we use Face Recognition Grand Challenge (FRGC) ver 2.0 experimental protocols and database. The proposed method shows an average of 90.67% verification rate on 2D face images under uncontrolled environmental variations.