Despite steadily increasing computational power, increasing numbers of subjects in data sets can cause identification systems such as face recognition to take a unreasonable long time to produce results. This is mainly due to the fact that the systems have to compare the input subject to every person contained in the data set in order to perform a comprehensive identification. In this thesis the so-called “morph acceleration” method is covered, which is able to reduce the number of comparisons needed for identification by fusing subjects at the sample level. Previous experiments on this method have shown that it is possible to reduce the number of comparisons, but the identification rate is also reduced. In this thesis alternative methods for the morph pair selection are proposed and tested on various combinations of face recognition and morphing systems. These methods enable the morphing acceleration to improve the morph-acceleration system in such a way, that a reduction of comparisons can be achieved without a loss in identification rate.