Great amount of work is being done in the domain of facial expression (FE) recognition. Of particular interest is a FE being at the very base of mother-baby interaction [1], a FE interpreted unequivocally in all human cultures [2] - smile. Maybe because of these reasons, maybe because of some others, smile detection is already of certain interest for computer vision (CV) community – be it for camera's smile shutter [3] or in order to study robot2children interaction [4].

Nonetheless, a publicly available i.e. open source, smile detector is missing. This is somewhat stunning, especially given the fact that “smile” can be conceived as a “blocky” object [5] upon which a machine learning technique based on training of cascades of boosted haar-feature classifiers [6] can be applied, and that the tools for performing such a training are already publicly available as part of an OpenCV[5] project. Verily, with exceptions of detectors described in [7][8] which have not been publicly released, we did not find any reference to haarcascade-based smile detector in the literature. We aim to address this issue by making publicly available the initial results of our attempts to construct sufficiently descriptive SMILing Multisource Incremental-Learning Extensible Sample (SMILEs) and five smile detectors (smileD) generated from this sample.

From more general perspective, our aim was to study whether one can use already generated classifiers in order to facilitate such a semi-supervised extension of initial sample that a more accurate classifier can be subsequently trained.