Adaptive Feature and Score Level Fusion Strategy Using Genetic Algorithms

Classifier fusion is considered as one of the best strategies for improving performance of general purpose classification systems. On the other hand, fusion strategy space strongly depends on classifiers, features and data spaces. As the cardinality of this space is exponential, one needs to resort to a heuristic to find a sub-optimal fusion strategy. In this work, we present a new adaptive feature and score level fusion strategy (AFSFS) based on adaptive genetic algorithm. AFSFS tunes itself between feature and matching score level, and improves the final performance over the original on both levels, and as a fusion method, it does not only contain fusion strategy to combine the most relevant features so as to achieve adequate and optimized results, but also has the extensive ability to select the most discriminative features. Experiments are provided on the FRGC database showing that the proposed method produces significantly better results than the baseline fusion methods.