In this paper we propose a data augmentation methodology for training machine/deep learning gait recognition algorithms. While previously published methods generated synthetic data to augment training and/or testing sets [1] or to learn features invariant to certain conditions [2], they have incorporated a very limited number of covariate factors. To our knowledge, this is the first attempt to provide the ability to simultaneously generate synthetic data with so many controllable conditions for gait recognition. The combination of real motion capture, data preparation, avatar construction and scripted rendering allows synthetic frames to be generated, with almost arbitrary degrees of variation. Figure 1 shows a small sample of the controllable confounding factors that can be generated with the proposed methodology. Figure 1: A small sample of the controllable confounding factors. It is readily apparent how the extracted gait features are directly affected. Using the Gait Energy Image (GEI) [3] as gait features, extracted directly from the synthetically generated frames, we performed experiments to assess the level to which identity is preserved within the synthetic data sets. Results from our experiments -presented in Figure  2 -suggest that information about the identity of subjects is retained within the synthetically generated data. The experiments using GEIs as features within our dataset showed that augment-ing a limited amount of real data with the synthetically generated data can yield identification of subjects with an accuracy of more than 95%. The results of this study suggest that synthetic data can be used to augment the training of gait recognition algorithms provided that the feature set (e.g. principal components) is expanded to include both real and synthetic examples of data.
In REF , e.g., Charalambous and Bharath employ guidedaugmentation in the context of gait recognition.
A data augmentation methodology for training machine/deep learning gait recognition algorithms
Procedings of the British Machine Vision Conference 2016
Procedings of the British Machine Vision Conference 2016
Computer Science