  This paper addresses the problem of providing robust estimators under a
functional logistic regression model. Logistic regression is a popular tool in
classification problems with two populations. As in functional linear
regression, regularization tools are needed to compute estimators for the
functional slope. The traditional methods are based on dimension reduction or
penalization combined with maximum likelihood or quasi--likelihood techniques
and for that reason, they may be affected by misclassified points especially if
they are associated to functional covariates with atypical behaviour. The
proposal given in this paper adapts some of the best practices used when the
covariates are finite--dimensional to provide reliable estimations. Under
regularity conditions, consistency of the resulting estimators and rates of
convergence for the predictions are derived. A numerical study illustrates the
finite sample performance of the proposed method and reveals its stability
under different contamination scenarios. A real data example is also presented.
