Unsupervised learning of face detection models from unlabeled image streams
Modern artificial face detection shows impressive performance in a variety of application areas. This success comes at the cost of supervised training, using large-scale databases provided by human experts. In this paper, we propose a face detection system based on Organic Computing [vdM08] paradigms that acquires necessary domain knowledge autonomously and learns a conceptual model of the human face/head region. Performance of the novel approach is experimentally compared to state-of-the-art face detection, yielding competitive results in scenarios of moderate complexity.
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