Face and Street Detection with Asymmetric Haar Features

We present a system for object detection applied to multi-pose face detection and street detection. Our system presents two 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. Our system uses specialized detectors in different object orientations that are built using AdaBoost and the C4.5 rule induction algorithm. In addition, for face detection we use a skin color-segmentation scheme to reduce the search space. Experimental results using the CMU profile test set and the BioID frontal face test set, as well as our own multi-pose face test set, show that our system is competitive with other systems presented recently in the literature.