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In this paper, a method of combining face detectors is proposed, which is based on the geometry of the competing face detection results. The main idea of the method consists in finding groups of similar face detection results obtained by several algorithms and further averaging them. The combination result essentially depends on the number of algorithms that have fallen in each of the groups. The...
The use of multimodal biometric systems has been encouraged by the threat of spoofing, where an impostor fakes a biometric trait. The reason lies on the assumption that, an impostor must fake all the fused modalities to be accepted. Recent studies showed that there is a vulnerability of the existing fusion schemes in presence of attacks where only a subset of the fused modalities is spoofed. In this...
We address the problem of cohort based normalisation in multiexpert class verification. We show that there is a relationship between decision templates and cohort based normalisation methods. Thanks to this relationship, some of the recent features of cohort score normalisation techniques can be adopted by decision templates, with the benefit of noise reduction and the ability to compensate for any...
We show that using random forests and distance-based outlier partitioning with ensemble voting methods for supervised learning of anomaly detection provide similar accuracy results when compared to the same methods without partitioning. Further, distance-based outlier and one-class support vector machine partitioning and ensemble methods for semi-supervised learning of anomaly detection also compare...
Over the last decade learning to rank (L2R) has gained a lot of attention and many algorithms have been proposed. One of the most successful approach is to build an algorithm following the ensemble principle. Boosting is the key representative of this approach. However, even boosting isn’t effective when used to increase the performance of individually strong algorithms, scenario when we want to blend...
Recently, ensemble techniques have also attracted the attention of Genetic Programing (GP) researchers. The goal is to further improve GP classification performances. Among the ensemble techniques, also bagging and boosting have been taken into account. These techniques improve classification accuracy by combining the responses of different classifiers by using a majority vote rule. However, it is...
During the last years, there has been an increasing interest in applying the multiple classifier framework to the domain of structural pattern recognition. Constructing base classifiers when the input patterns are graph based representations is not an easy problem. In this work, we make use of the graph embedding methodology in order to construct different feature vector representations for graphs...
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