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Gait as a behavioral biometric has been the subject of recent investigations. However, understanding the limits of gait-based recognition and the quantitative study of the factors effecting gait have been confounded by errors in the extracted silhouettes, upon which most recognition algorithms are based. To enable us to study this effect on a large population of subjects, we present a novel model based silhouette reconstruction strategy, based on a population based hidden Markov model (HMM), coupled with an eigen-stance model, to correct for common errors in silhouette detection arising from shadows and background subtraction. The model is trained and benchmarked using manually specified silhouettes for 71 subjects from the recently formulated HumanID Gait Challenge database. Unlike other essentially pixel-level silhouette cleaning methods, this method can remove shadows, especially between feet for the legs-apart stance, and remove parts due to any objects being carried, such as briefcase or a walking cane. After quantitatively establishing the improved quality of the silhouette over simple background subtraction, we show on the 122 subjects HumanID Gait Challenge Dataset and using two gait recognition algorithms that the observed poor performance of gait recognition for hard problems involving matching across factors such as surface, time, and shoe are not due to poor silhouette quality, beyond what is available from statistical background subtraction based methods.