Gait depicts individuals’ unique and distinguishing walking patterns and has become one of the most promising biometric features for human identification. As a fine-grained recognition task, gait recognition is easily affected by many factors and usually requires a large amount of completely annotated data that is costly and insatiable. This paper proposes a large-scale self-supervised benchmark for gait recognition with contrastive learning, aiming to learn the general gait representation from massive unlabelled walking videos for practical applications via offering informative walking priors and diverse real-world variations. Specifically, we collect a large-scale unlabelled gait dataset GaitLU-1M consisting of 1.02M walking sequences and propose a conceptually simple yet empirically powerful baseline model GaitSSB. Experimentally, we evaluate the pre-trained model on four widely-used gait benchmarks, CASIA-B, OU-MVLP, GREW and Gait3D with or without transfer learning. The unsupervised results are comparable to or even better than the early model-based and GEI-based methods. After transfer learning, GaitSSB outperforms existing methods by a large margin in most cases, and also showcases the superior generalization capacity. Further experiments indicate that the pre-training can save about 50% and 80% annotation costs of GREW and Gait3D. Theoretically, we discuss the critical issues for gait-specific contrastive framework and present some insights for further study. As far as we know, GaitLU-1M is the first large-scale unlabelled gait dataset, and GaitSSB is the first method that achieves remarkable unsupervised results on the aforementioned benchmarks.

Recently gait recognition receives increasing attention since it can be conducted at a long distance without the cooperation of subjects and suit for the cases of changing clothes. A key challenge is to learn gait features from the silhouettes that are invariant to the external factors such as clothing, carrying conditions and camera viewpoints. In this work, we propose a Set Residual Network for gait recognition which tries to learn more discriminative features from the silhouettes. Specifically, the silhouettes of each gait sequence are regarded as an unordered set and we propose a Set Residual Block to extract the silhouette-level and set-level features in a parallel way. Particularly, a residual connection is adopted to connect the two-level features inside each block, which enables more silhouette-set interaction and effectively coordinates the silhouette-level and set-level information for set-based feature learning from the silhouettes. Besides, we propose an efficient strategy to exploit the features from the shallow layers to learn more robust part representations for gait recognition, where the upsampling or lateral connections are unnecessary and only marginal memory cost is required. The experiments on CASIA-B and OUMVLP show that our approach can bring consistent improvement over the baselines for all walking conditions.

Machine unlearning as a fundamental requirement in Machine-Learning-as-a-Service (MLaaS) has been extensively studied with increasing concerns about data privacy. It requires MLaaS providers should delete training data upon user requests. Unfortunately, none of the existing studies can efficiently achieve machine unlearning validation while preserving the retraining efficiency and the service quality after data deletion. Besides, how to craft the validation scheme to prevent providers from spoofing validation by forging proofs remains under-explored. In this paper, we introduce a backdoor-assisted validation scheme for machine unlearning. The proposed design is built from the ingenious combination of backdoor triggers and incremental learning to assist users in verifying proofs of machine unlearning without compromising performance and service quality. We propose to embed invisible markers based on backdoor triggers into privacy-sensitive data to prevent MLaaS providers from distinguishing poisoned data for validation spoofing. Users can use prediction results to determine whether providers comply with data deletion requests. Besides, we incorporate our validation scheme into an efficient incremental learning approach via our index structure to further facilitate the performance of retraining after data deletion. Evaluation results on real-world datasets confirm the efficiency and effectiveness of our proposed verifiable machine unlearning scheme.