Kinect-Based Gait Analysis for People Recognition Over Time

Abstract

Gait has recently attracted a great interest in the biometric field because, contrary to other classical biometric traits such as fingerprint, iris or retina, it allows to capture samples at a distance, through inexpensive and not intrusive technologies that do not need any subject's collaboration. In spite of this advantage, such technique is still not widespread for human identification task, because it is considered not to exhibit the fundamental characteristic of being invariant in the lifetime of each individual. But is this assertion really true? In this paper we investigate if gait can be considered invariant over time for an individual, at least in a time interval of few years, by comparing gait samples of several subjects three years apart. We train a Support Vector Machine with gait samples of 10 subjects, then we employ it for recognizing the same subjects with gait samples collected three years later. In addition, we try to recognize the subjects carrying three different accessories: a shoulder bag, a backpack and a smartphone.