Abstract
Gait information can be collected by a long-distance camera. But the relative angle between the subject and the camera changes, resulting in a cross-view gait recognition problem. This paper proposes a view transformation model method based on feature separation generate adversarial networks. Based on the GAN model, this method separates the features of the input data as an additional discriminant basis. On the premise of building a single model, it can convert image to any angle as needed. In order to make the images generated by GAN more realistic, the proposed method separates view and dress information from the identity data and encodes them. The discriminator is also optimized by adding the conditional codes as an additional basis, so that the generator can generate the corresponding image more realistically based on the encoded information image. In addition, the proposed method also adds a constraint to increase the inter-class variation of subjects and reduce their intra-class distance. Thus, the synthesized image retains more feature information of original subject. The proposed method achieves a great generating effect and improves the performance of cross-view gait recognition.
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Huang, C., Song, Y., Zhang, Y. (2021). Feature Separation GAN for Cross View Gait Recognition. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_6
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DOI: https://doi.org/10.1007/978-3-030-87355-4_6
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