IEEE Transactions on Pattern Analysis and Machine Intelligence
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combination of Face Classifiers for Person Identification
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume III-Volume 7276 - Volume 7276
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
An Improved Learning Scheme for the Moving Window Classifier
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
A multidimensional hybrid intelligent method for gear fault diagnosis
Expert Systems with Applications: An International Journal
PCA based immune networks for human face recognition
Applied Soft Computing
International Journal of Knowledge-based and Intelligent Engineering Systems
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In this paper we present two methods to create multiple classifier systems based on an initial transformation of the original features to the binary domain and subsequent decompositions (quantisation). Both methods are generally applicable although in this work they are applied to grey-scale pixel values of facial images which form the original feature domain. We further investigate the issue of diversity within the generated ensembles of classifiers which emerges as an important concept in classifier fusion and propose a formal definition based on statistically independent classifiers using the @k statistic to quantitatively assess it. Results show that our methods outperform a number of alternative algorithms applied on the same dataset, while our analysis indicates that diversity among the classifiers in a combination scheme is not sufficient to guarantee performance improvements. Rather, some type of trade off seems to be necessary between participant classifiers' accuracy and ensemble diversity in order to achieve maximum recognition gains.