Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Analytic-to-Holistic Approach for Face Recognition Based on a Single Frontal View
IEEE Transactions on Pattern Analysis and Machine Intelligence
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
The M2VTS Multimodal Face Database (Release 1.00)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
Generalizing Capacity of Face Database for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Journal of Cognitive Neuroscience
Face recognition: a convolutional neural-network approach
IEEE Transactions on Neural Networks
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Databases play an important role for the development and evaluation of methods for person identification, verification, and other tasks. Despite this fact, there exists no measure that indicates whether a given database is sufficient to train and/or to test a given algorithm. This paper proposes a method to rank the complexity of databases, respectively to validate whether a database is appropriate for the simulation of a given application. The first nearest neighbor and the mean square distance are validated to be suitable as minimal performance measures with respect to the problems of person verification and person identification.