Face recognition performance analysis: Cohort classifiers

In this report we will describe how we researched the performance of traditional face recognition methods in combination with clustering.The idea is that the performance of a trained PCA/LDA classifier can be improved with a two-step approach. With the first step, faces are bundled into clusters. After that, a face recognition system is trained individually on each cluster. There are different methods for clustering, like using PCA and then using the Euclidean Distance to determine which faces are close. Or just use a face-recognition system to determine which faces are close. By using one of these approaches, one can create clusters easily. These clusters are called cohorts