This paper presents the use of a multi-linear classifier allowing to fuse the results of several modalities in a multi-modal person identity verification context. In the considered verification system, each of the d modalities forms an autonomous bloc that produces a score, which is not only supposed to be monotone but also to have a value between zero and one. The fusion module that we are discussing here takes a binary decision: accept or reject the identity claimed by the person, based on the whole of the scores given in parallel by all d modalities. To realize this fusion module we have developed a classifier that, on the one hand, accepts the monotonicity hypothesis and, on the other hand, is based on separating the classes (accept-reject) by a combination of half-spaces, a technique from which it derived its name. The classifier is trained using couples formed by extracting an example from each class and the half-spaces are determined by maximizing a global separability measure of the thus formed couples. Afterwards, each region of the partition of the d dimensional space, generated by the half-spaces, is labeled with the corresponding class, using the Logical Analysis of Data (LAD) method. The performance of the developed multi-linear classifier has been evaluated on multi-modal experimental data and the obtained results are presented.