Active Shape Model and linear predictors for face association refinement

This paper summarizes results of face association experiments on real low resolution data from airport and the Labeled faces in the Wild (LFW) database. The objective of experiments is to evaluate different face alignment methods and their contribution to face association as such. The first alignment method used is Sequential Learnable Linear Predictor (SLLiP), originally developed for object tracking. The second method is well known face alignment method Active Shape Model (ASM). Both methods are compared versus face association without alignment. In case of high resolution LFW database the ASM rapidly increases the association results, on the other hand for real low resolution airport data the SLLiP method brought more improvement than ASM.