An efficient 3D face recognition approach based on the fusion of novel local low-level features
We present a novel 3D face recognition approach based on low-level geometric features that are collected from the eyes-forehead and the nose regions. These regions are relatively less influenced by the deformations that are caused by facial expressions. The extracted features revealed to be efficient and robust in the presence of facial expressions. A region-based histogram descriptor computed from these features is used to uniquely represent a 3D face. A Support Vector Machine (SVM) is then trained as a classifier based on the proposed histogram descriptors to recognize any test face. In order to combine the contributions of the two facial regions (eyes-forehead and nose), both feature-level and score-level fusion schemes have been tested and compared. The proposed approach has been tested on FRGC v2.0 and BU-3DFE datasets through a number of experiments and a high recognition performance was achieved. Based on the results of neutral vs. non-neutral experiment of FRGC v2.0 and low-intensity vs. high-intensity experiment of BU-3DFE, the feature-level fusion scheme achieved verification rates of 97.6% and 98.2% at 0.1% False Acceptance Rate (FAR) and identification rates of 95.6% and 97.7% on the two datasets respectively. The experimental results also have shown that the feature-level fusion scheme outperformed the score-level fusion one.
3d face recognition svm score-level fusion feature-level fusion 3d geometric feature