3D face recognition based on the hierarchical score-level fusion classifiers

This paper describes the 3D face recognition algorithm that is based on the hierarchical score-level fusion classifiers. In a simple (unimodal) biometric pipeline, the feature vector is extracted from the input data and subsequently compared with the template stored in the database. In our approach, we utilize several feature extraction algorithms. We use 6 different image representations of the input 3D face data. Moreover, we are using Gabor and Gauss-Laguerre filter banks applied on the input image data that yield to 12 resulting feature vectors. Each representation is compared with corresponding counterpart from the biometric database. We also add the recognition based on the iso-geodesic curves. The final score-level fusion is performed on 13 comparison scores using the Support Vector Machine (SVM) classifier.