On including quality in applied automatic gait recognition

Many gait recognition approaches use silhouette data. Imperfections in silhouette extraction have a negative effect on the performance of a gait recognition system. In this paper we extend quality metrics for gait recognition and evaluate new ways of using quality to improve a recognition system. We demonstrate use of quality to improve silhouette data and select gait cycles of best quality. The potential of the new approaches has been demonstrated experimentally on a challenging dataset, showing how recognition capability can be dramatically improved. Our practical study also shows that acquiring samples of adequate quality in arbitrary environments is difficult and that including quality analysis can improve performance markedly.