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Feature extraction based on different types of signal filters has received a lot of attention in the context of face recognition. It generally results into extremely high dimensional feature vectors, and sampling of the coefficients is required to reduce their dimensionality. Unfortunately, uniform sampling that is commonly used to that aim, does not consider the specificities of the recognition task in selecting the most relevant features. In this paper, we propose to formulate the sampling problem as a supervised feature selection problem where features are carefully selected according to a well defined discrimination criterion. The sampling process becomes specific to the classification task, and further facilitates the face recognition operations. We propose to build features on random filters, and Gabor wavelets, since they present interesting characteristics in terms of discrimination, due to their high frequency components. Experimental results show that the proposed feature selection method outperforms uniform sampling, and that random filters are very competitive with the common Gabor wavelet filters for face recognition tasks.