MELIS , Marco

Many modern face verification algorithms use a small set of reference templates to save memory and computational resources. However, both the reference templates and the combination of the corresponding matching scores are heuristically chosen. In this paper, we propose a well-principled approach, named sparse support faces, that can outperform...

In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors. In these settings, machine-learning algorithms can become very computationally demanding, as they may require matching the test samples against a very large set of reference prototypes. To...

Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Machines (SVMs) have been successfully exploited to tackle this problem, using one-vs-one or one-vs-all learning schemes to enable multiclass classification, and kernels designed for image classification to handle nonlinearities. To classify an...