Intelligent three step sampling design for face recognition

Learning curve phenomenon indicates that not all available images need to be used in training. This paper proposes a three-step intelligent sampling to construct a representative and efficient training database, where both the number of training images and which images to be included are determined. Firstly, clustering on a subset of huge face database is implemented as preparation. Secondly, systematic sampling on clusters is utilized to improve the efficiency. Thirdly, performance is evaluated to check whether the learning curve has reached a point of diminishing returns, and a new metric of difficulty is defined to determine which images from the complementary subset of initial training set should be added into training. The proposed intelligent three step sampling design enhances recognition rate and generalizability while improving efficiency, which exerts the full potential of any given face recognition algorithm without system overhaul.