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.

► A 3D facial scan is divided into different expression-sensitive regions. ► The extracted local facial surface is represented by multiple spatial triangles. ► Four types of low-level geometric features are developed based on those triangles. ► SVM is applied to perform face recognition based on the fusion of local features. ► Both feature and score-level fusion are used to obtain the final recognition result.