%0 Electronic Article %A Abhishek Dutta %+ Faculty of EEMCS, University of Twente, P.O. Box 217, 7500 AE, Enschede, Netherlands %A Manuel Günther %+ Centre du Parc, Idiap Research Institute, Rue Marconi 19, P.O. Box 592, CH 1920, Martigny, Switzerland %A Laurent El Shafey %+ Centre du Parc, Idiap Research Institute, Rue Marconi 19, P.O. Box 592, CH 1920, Martigny, Switzerland %A Sébastien Marcel %+ Centre du Parc, Idiap Research Institute, Rue Marconi 19, P.O. Box 592, CH 1920, Martigny, Switzerland %A Raymond Veldhuis %+ Faculty of EEMCS, University of Twente, P.O. Box 217, 7500 AE, Enschede, Netherlands %A Luuk Spreeuwers %+ Faculty of EEMCS, University of Twente, P.O. Box 217, 7500 AE, Enschede, Netherlands %K automatic eye detectors %K open source implementations %K facial feature alignment %K face recognition performance %K error characteristics %K eye detection %K commercial face recognition systems %K query phases %K face normalisation %K manual eye annotations %K face recognition algorithms %K eye localisation errors %X The locations of the eyes are the most commonly used features to perform face normalisation (i.e. alignment of facial features), which is an essential preprocessing stage of many face recognition systems. In this study, the authors study the sensitivity of open source implementations of five face recognition algorithms to misalignment caused by eye localisation errors. They investigate the ambiguity in the location of the eyes by comparing the difference between two independent manual eye annotations. They also study the error characteristics of automatic eye detectors present in two commercial face recognition systems. Furthermore, they explore the impact of using different eye detectors for training/enrolment and query phases of a face recognition system. These experiments provide an insight into the influence of eye localisation 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. %@ 2047-4938 %T Impact of eye detection error on face recognition performance %B IET Biometrics %D September 2015 %V 4 %N 3 %P 137-150 %I Institution of Engineering and Technology %U https://digital-library.theiet.org/;jsessionid=me502p18s5p4.x-iet-live-01content/journals/10.1049/iet-bmt.2014.0037 %G EN