Recently many automatic face recognition (AFR) systems were developed for applications with unspecific persons, which is different from conventional pattern recognition problems where all classes are known in the training stage. In this paper, we present a systematic and comprehensive study on linear subspace methods for face recognition on unspecific persons. Over 6700 experiments using different algorithms with different training parameters and testing conditions are conducted on a large scale database (4550 samples) to investigate the compound effect of various influential factors. The observations based on these experiments are expected to provide widely applicable guidelines for designing practical AFR systems.