Impact of eye detection error on face recognition performance

The locations of the eyes are the most commonly used features to perform face normalization (i. e., alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this paper, we study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye localization errors. We investigate the ambiguity in location of the eyes by comparing the difference between two independent manual eye annotations. We also study the error characteristics of automatic eye detectors present in two commercial face recognition systems. Furthermore, we explore the impact of using different eye detectors for training/enrollment and query phases of a face recognition system. These experiments provide an insight into the influence of eye localization errors on the performance of face recognition systems and recommend a strategy for the design of training and test sets of a face recognition algorithm.