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It has been folklore that humans can identify others based on their biological movement from a distance. This observation was somewhat bolstered by experiments with light point displays by human perception researchers in the 70s and have been confirmed by recent human perception experiments. However, it is only recently that computer vision based gait biometrics has received much attention. Recent research on this topic, much of it facilitated by the structure of the DARPA HumanID Gait Challenge Problem, has brought into light interesting capabilities and limits of this modality. Recognition is possible from gait.
The tutorial will start by describing how this challenge framework, consisting of data sets, challenge experiments, and a baseline performance, has helped jump start the gait recognition area. It will also summarize some of the lessons learned in terms of what are the sources of gait variations that are easy to overcome and what are still the outstanding ones. Perhaps from a vision point of view one of the important observations that some researchers have made is that gait shapes offer more stable cues for recognition, across different covariates, than gait dynamics. Building on these observations, we will summarize an approach that first performs gait dynamics normalization using population HMM and then computes distances between gait shapes in a space that maximizes differences between individuals. This algorithm statistically improves recognition over all covariates in the DARPA HumanID Gait Challenge Problem.
Other possible biometrics that can be captured at a distance is face and voice, i.e. automatic speaker recognition, which is a mature research area. Current challenges lie in the area of voice recognition at remote distances using readily available remote microphones or microphone arrays. Other research avenues include making the speaker recognition system robust to background noise and microphone type. The tutorial will end by presenting some ideas from biometric fusion to improve recognition at a distance.