Abstract

This paper introduces a new approach, called reverse train- ing, to efficiently extend binary classifiers for the task of multi-class image set classification. Unlike existing binary to multi-class extension strate- gies, which require multiple binary classifiers, the proposed approach is very efficient since it trains a single binary classifier to optimally discrim- inate the class of the query image set from all others. For this purpose, the classifier is trained with the images of the query set (labelled positive) and a randomly sampled subset of the training data (labelled negative). The trained classifier is then evaluated on rest of the training images. The class of these images with their largest percentage classified as positive is predicted as the class of the query image set. The confidence level of the prediction is also computed and integrated into the proposed approach to further enhance its robustness and accuracy. Extensive experiments and comparisons with existing methods show that the proposed approach achieves state of the art performance for face and ob ject recognition on a number of datasets.