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Gait recognition using noninvasively acquired data has been attracting anincreasing interest in the last decade. Among various modalities of datasources, it is experimentally found that the data involving skeletalrepresentation are amenable for reliable feature compaction and fastprocessing. Model-based gait recognition methods that exploit features from afitted model, like skeleton, are recognized for their view and scale-invariantproperties. We propose a model-based gait recognition method, using sequencesrecorded by a single flash lidar. Existing state-of-the-art model-basedapproaches that exploit features from high quality skeletal data collected byKinect and Mocap are limited to controlled laboratory environments. Theperformance of conventional research efforts is negatively affected by poordata quality. We address the problem of gait recognition under challengingscenarios, such as lower quality and noisy imaging process of lidar, thatdegrades the performance of state-of-the-art skeleton-based systems. We presentGlidarCo to attain high accuracy on gait recognition under the describedconditions. A filtering mechanism corrects faulty skeleton joint measurements,and robust statistics are integrated to conventional feature moments to encodethe dynamic of the motion. As a comparison, length-based and vector-basedfeatures extracted from the noisy skeletons are investigated for outlierremoval. Experimental results illustrate the efficacy of the proposedmethodology in improving gait recognition given noisy low resolution lidardata.

GlidarCo: gait recognition by 3D skeleton estimation and biometric feature correction of flash lidar data