An Experimental Evaluation of Three Classifiers for Use in Self-Updating Face Recognition Systems

Previous studies have shown that the accuracy of Face Recognition Systems (FRSs) decreases with the time elapsed between enrollment and testing. The main reason for the decrease is the changes in appearance of the user due to factors such as ageing, beard growth, sun-tan etc. Self-update procedure, where the system learns the biometric characteristics of the user every time he/she interacts with it, can be used to automatically update the system. However, a commonly acknowledged problem is the corruption of biometric traits due to misclassification. In this article, we test FRS, based on three classification algorithms, on two challenging databases, GEFA and YT, with 14 279 and 31 951 images, respectively. Our results suggest that complex, state-of-the-art classifiers that make use of user-specific models, need not be the best choice for use in self updating systems. In other words, tolerance to corrupted training data decreases as the complexity of the classifier increases.