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Robots that cooperate and interact with humans require the capacity to detect and track people, analyze their behavior and understand human social relations and rules. A key piece of information for such tasks are human attributes like gender, age, hair or clothing. In this paper, we address the problem of recognizing such attributes in RGB-D data from varying full-body views. To this end, we extend a recent tessellation boosting approach which learns the best selection, location and scale of adoi:10.1109/iros.2015.7353541 dblp:conf/iros/LinderA15 fatcat:di6fyyzlorcmzbefdkuznwx7c4