Abstract
In this paper we present two algorithms for efficient person recognition operating upon motion capture data, depicting persons performing various everyday activities. The first approach is driven from the assumption that, if two motion sequences depict a certain activity performed by the same person, then, consecutive frames (poses) of one sequence are expected to be similar to consecutive frames of the other. The proposed method constructs a pose correspondence matrix to represent the similarity between poses and utilizes an intuitive method for estimating a similarity score between two motion capture sequences, based on the structure of the correspondence matrix. The second algorithm is based on a Bag of Words model (BoW), where histograms are extracted from motion sequences, based on the frequency of occurrences of characteristic poses. This method is combined with the application of Locality Preserving Projections (LPP) on the data, in order to reduce their dimensionality. Our methods achieved more than 98% correct person recognition rate, in three different datasets.
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