Robust Cross-View Gait Identification with Evidence: A Discriminant Gait GAN Approach
Gait is an important biometric trait for surveillance and forensic applications, which can be used to identify individuals at a large distance through CCTV cameras. However, it is very difficult to develop robust automated gait recognition systems, since gait may be affected by many covariate factors such as clothing, walking surface, walking speed, camera view angle, etc.
Out of them, large view angle was deemed as the most challenging factor since it may alter
the overall gait appearance substantially. Recently, some deep learning approaches (such as CNNs) have been employed to extract view-invariant features, and achieved encouraging
results on small data sets.
However, they do not scale well to large dataset, and the performance decreases significantly w.r.t. number of subjects, which is impractical to large-scale surveillance applications.
To address this issue, in this work we propose a Discriminant Gait Generative Adversarial Network (DiGGAN) framework, which not only can learn view-invariant gait features for crossview gait recognition tasks, but also can be used to reconstruct the gait templates in all views — serving as important evidences for forensic applications.
We evaluated our DiGGAN framework on the world’s largest multi-view OU-MVLP dataset (which includes more than 10,000 subjects), and our method outperforms state-of-the-art algorithms significantly on various cross-view gait identification scenarios (e.g., cooperative/uncooperative mode).
Our DiGGAN framework also has the best results on the popular CASIA-B dataset, and it shows great generalisation capability across different datasets.
This paper studied a challenging large-scale cross-view gait recognition problem. Using GANs to generate different views, the learnt latent embedding achieved remarkable cross-view transferability. The model effectively incorporated three modules.
The Dangle loss provided an interactive interface through which a given arbitrary view could be used to generate all of other views. The Did loss preserves identity sensitive information in the generated images.
To further discriminate a large number of identities, triplet constraint was introduced onto the latent embedding. Moreover, since the triplet training incorporated images from different views, the inter-identity distance was enlarged, which further de-correlated effects of the crossview problem.
Extensive experiments manifested promising improvements over the state-of-the-arts. Our method also achieved the best results in the non-cooperative scenario, which has non-uniform views in the gallery. More reliable performance was achieved in small-scale datasets (i.e. CASIA-B) and we further show our DiGGAN framework can effectively take advantage of large dataset for cross dataset generalisation.
Detailed training strategy was discussed so as the model could benefit both gait recognition domain and experts of other domains who would use GANs to solve their problems. Overall, this paper made a breakthrough towards reliable cross-view gait recognition at a very large scale with generated evidence for practical applications.
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