Individual recognition from periodic activity using hidden Markov models

We present a method for recognizing individuals from their walking and running gait. The method is based on Hu moments of the motion segmentation in each frame. Periodicity is detected in such a sequence of feature vectors by minimizing the sum of squared differences, and the individual is recognized from the feature vector sequence using hidden Markov models. Comparisons are made to earlier periodicity detection approaches and to earlier individual recognition approaches. Experiments show the successful recognition of individuals (and their gait) in frontoparallel sequences.