Regional confidence score assessment for 3D face
3D shape data for face recognition is advantageous to its 2D counterpart for being invariant to illumination and pose. However, expression variations and occlusions still remain as major challenges since the shape distortions hinder accurate matching. Numerous algorithms developed to overcome this problem mainly propose region-based approaches, where similarity scores are calculated separately by local regional matchers and fused for recognition. In this paper, we present a regional confidence score assessment scheme that estimates the expression or occlusion induced distortions in different facial regions. Thereby, reliability scores are obtained which can be used in fusion step for recognition. For 7 regions of face, primitive shape distributions are extracted and the surface quality is measured automatically by an Iterative Closest Point (ICP) based method. Using these measurements, an Artificial Neural Network (ANN) is trained and utilized to estimate regional reliability scores. Experiments have been conducted on FRGC v2 3D face database and results demonstrate a high accuracy in surface quality estimation.