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A human posture recognition system based on a memory-based approach is studied. Human body images extracted by depth data are labeled and stored in a database together with compressed feature values consist of higher-order local correlation values and outline diameters. The compressed features are used to speed up the database search in a hierarchical manner. The system can classify human body postures into 6 categories in the experiment. The system was robust against change of humans and light condition.