A novel training weighted ensemble (TWE) with application to face recognition

Individual classifiers that are fully trained are unstable especially when the database conditions are changed. Moreover, designing a unique classifier with the suitable parameters to achieve acceptable performance is a non-trivial task. Combined classifiers, which consist of a set of individually trained classifiers, are introduced to avoid the previous problems. There are two key issues in the combination of classifiers. The first issue is how to obtain the set of base classifiers to combine. The second issue is how to fuse the decisions of those classifiers. In this paper, weak Learning Vector Quantization (LVQ) neural networks have been used as base classifiers. Also, a new combination technique which is based on training-weighted voting is introduced. Other factors that greatly affect the performance of a combined classifier are related to the type of the individual classifiers, the training parameters, database size and nature, etc. These factors have been considered in the design of the proposed combined classifier. TWE has been experimentally tested on five standard face databases: Yale, ORL, Grimace, Faces94 and Faces95 and has demonstrated excellent performance. Analysis of the ensemble stability has shown promising results.