Local Attention and Global Representation Collaborating for Fine-grained Classification
He Zhang, Yunming Bai, Hui Zhang, Jing Liu, Xingguang Li, Zhaofeng He · 2021
The cosmetic contact lenses over an iris may change original iris textural pattern which is the foundation for iris recognition, making the cosmetic lenses a possible and easy-to-use iris presentation attack means. For practical application scenes, the cosmetic contact lenses detection still facing unsolved problems, due to the low image quality and difficulty in accurately iris localization. In this paper, we propose a novel framework called Weighted Region Network (WRN) to detect the cosmetic contact lenses. The WRN includes a local attention Weight Network and a global classification Region Network. With the inherent attention mechanism, the proposed network is able to find more discriminative regions, which reduces the requirement for target detection and improves the ability of classification. The Weight Network can be trained by using Rank loss and MSE loss without manual discriminative region annotations. Experiments are conducted on several public databases and a new collected low-quality iris image database. The proposed method outperforms state-of-the-art fake iris detection algorithms, and is also effective for the fine-grained image classification task.