Adaptive Rigid Multi-region Selection for Handling Expression Variation in 3D Face Recognition

We present a new algorithm for 3D face recognition, and compare its performance to that of previous approaches. We focus especially on the case of facial expression change between gallery and probe images. We first establish performance comparisons using a PCA ("eigenface") algorithm and an ICP (iterative closest point) algorithm similar to ones reported in the literature. Experimental results show that the performance of either approach degrades substantially in the case Then we introduce a new algorithm, Adaptive Rigid Multi-region Selection, is introduced to independently matches multiple facial regions and creates a fused result. This algorithm is fully automated and used no manually selected landmark points. Experimental results show that our new algorithm substantially improves performance in the case of varying facial expression. Our experimental results are based on the largest 3D face dataset to date, with 449 persons, over 4,000 3D images, and substantial lapse between gallery and probe images.