Boosting Radial Strings for 3D Face Recognition with Expressions and Occlusions
Xun Bo Yu, Yongsheng Gao, Jun Wei Zhou · 2016
In this paper, we present a new radial string representation and matching approach for 3D face recognition under expression variations and partial occlusions. The radial strings are an indexed collection of strings emanating from the nose tip of a face scan. The matching between two radial strings is conducted through a dynamic programming process, in which a partial matching mechanism is established to effectively find those un-occluded substrings. Moreover, the most discriminative and stable radial strings are selected optimally by the well-known AdaBoost algorithm to achieve a composite classifier for 3D face recognition under facial expression changes. Experimental results on the GavabDB and the Bosphorus databases show that the proposed approach achieves promising results for human face recognition with expressions and occlusions.