Direction-Free Person Identification with Distance Transform and Random Forest

The problem of person identification is usually dealt with by explicitly extracting gait-related characteristics from input silhouettes. This paper describes another approach that does not perform such pre-defined and/or hand-crafted features to highlight useful properties. Unlike related works where the task of gait cycle extraction is required, our method directly employs a sliding window to process the temporal factor. In addition, we also introduce a scheme of combining identification results obtained under different camera directions in order to improve the system confidence. Our experiments on the CASIA-A gait dataset demonstrated the potential of the proposed approach and the obtained results were promising compared with related methods.

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