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The human face provides useful information during interaction, therefore any system integrating Vision Based Human Computer Interaction requires fast and reliable face and facial feature detection. Dierent approaches have focused on this ability but only open source implementations have been extensively used by researchers. A good example is the Viola-Jones object detection framework that particularly in the context of facial processing has been frequently used. The OpenCV community shares a collection of public domain classiers for the face detection scenario. However, these classiers have been trained in dierent conditions and with dierent data but rarely tested on the same datasets. In this paper we try to ll that gap by analyzing the individual performance of all those public classiers presenting their pros and cons with the aim of dening a baseline for other approaches. Solid comparisons will also help researchers to choose a specic classier for their particular scenario. The experimental setup also describes some heuristics to increase the facial feature detection rate while reducing the face false detection rate.Keywords face and facial feature detection · haar wavelets · human computer interaction · face datasets · OpenCV

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