In this paper we present a system for multi-pose face detection. Our system presents three main contributions. First, we introduce the use of asymmetric Haar features. Asymmetric Haar features provide a rich feature space, which allows to build classifiers that are accurate and much simpler than those obtained with other features. The second contribution is the use of a genetic algorithm to search efficiently in the extremely large parameter space of potential features. Using this genetic algorithm, we generate a feature set that allows to exploit the expressive advantage of asymmetric Haar features and is small enough to permit exhaustive evaluation. The third contribution is the application of a skin color-segmentation scheme to reduce the search space. Our system uses specialized detectors in different face poses that are built using AdaBoost and the C4.5 rule induction algorithm. Experimental results using the CMU profile test set and BioID frontal faces test set, in addition to our own multi-pose face test set, show that our system is competitive with other systems presented recently in the literature.