3D face recognition based on region ensemble and hybrid features
In this paper, we present a novel pose and expression invariant 3D face recognition approach based on intrinsic coordinate system (ICS) alignment. Motivated by the fact that a single classifier cannot be generally efficient against all face regions, a two tier region ensemble based classification approach is presented which employs three parallel face recognition algorithms using Mahalanobis Cosine (MahCos) matching score. The parallel face recognition algorithms employ Principal Component Analysis (PCA) based holistic features, Local Binary Patterns (LBP) based local features and modified Borda Count (MBC) based fusion technique respectively to classify the face images. The results obtained from the parallel algorithms are combined using an exponential rank reordering approach. The performance of the proposed methodology is corroborated by extensive experiments performed on FRGC v2.0 3D database. The results confirm that fusion strategies can be effectively used to construct a single classifier for improved performance.
2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube)