On how to improve tracklet-based gait recognition systems

• New algorithm to discard irrelevant tracklets for gait representation.
• Comparison of new RootDCS descriptor for gait representation against original DCS.
• Metric learning and binary representation for compact gait descriptors.
• Thorough experimental evaluation on standard datasets CASIA-B, CASIA-C and TUM-GAID.
• State-of-the-art results on verification and identification tasks.

Recently, short-term dense trajectories features (DTF) have shown state-of-the-art results in video recognition and retrieval. However, their use has not been extensively studied on the problem of gait recognition. Therefore, the goal of this work is to propose and evaluate diverse strategies to improve recognition performance in the task of gait recognition based on DTF. In particular, this paper will show that (i) the proposed RootDCS descriptor improves on DCS in most tested cases; (ii) selecting relevant trajectories in an automatic way improves the recognition performance in several situations; (iii) applying a metric learning technique to reduce dimensionality of feature vectors improves on standard PCA; and (iv) binarization of low-dimensionality feature vectors not only reduces storage needs but also improves recognition performance in many cases. The experiments are carried out on the popular datasets CASIA, parts B and C, and TUM-GAID showing improvement on state-of-the-art results for most scenarios.