Developing reliable iris recognition and presentation attack detection methods requires diverse datasets that capture realistic variations in iris features and a wide spectrum of anomalies. Because of the rich texture of iris images, which spans a wide range of spatial frequencies, synthesizing same-identity iris images while controlling specific attributes remains challenging. In this work, we introduce a new iris image augmentation strategy by traversing a generative model's latent space toward latent codes that represent same-identity samples but with some desired iris image properties manipulated. The latent space traversal is guided by a gradient of specific geometrical, textural, or quality-related iris image features (e.g., sharpness, pupil size, iris size, or pupil-to-iris ratio) and preserves the identity represented by the image being manipulated. The proposed approach can be easily extended to manipulate any attribute for which a differentiable loss term can be formulated. Additionally, our approach can use either randomly generated images using either a pre-train GAN model or real-world iris images. We can utilize GAN inversion to project any given iris image into the latent space and obtain its corresponding latent code.

翻译：开发可靠的虹膜识别与呈现攻击检测方法，需依赖涵盖虹膜特征真实变化及广泛异常谱系的多样化数据集。由于虹膜图像具有跨越多空间频率的丰富纹理，如何在保持同一身份的同时通过控制特定属性合成虹膜图像仍具挑战性。本文提出一种新型虹膜图像增强策略：通过遍历生成模型潜空间，将潜码定向至代表同一身份样本但具有所需虹膜图像属性扰动的区域。该潜空间遍历过程由特定几何、纹理或质量相关虹膜图像特征（如锐度、瞳孔直径、虹膜直径或瞳孔-虹膜比）的梯度引导，同时保持被操作图像的身份表征。该方案可便捷扩展至任何可构建可微损失项的属性操控。此外，本方法既可利用预训练生成对抗网络随机生成图像，也能处理真实虹膜图像——通过生成对抗网络反演将任意虹膜图像投影至潜空间并获取其对应潜码。