Generating Intra- and Inter-Class Iris Images by Identity Contrast

Abstract

Iris recognition is one of the most accurate and reliable biometric technologies. However, due to the high collection costs and privacy of the iris, it is difficult to build a large-scale iris image database for training iris recognition models. This paper proposes a novel iris image generation algorithm which can produce numerous intra- and inter-class iris images. By using contrastive learning, we disentangle identity-related features (e.g., iris texture, left or right eye) and condition-variant features (e.g., pupil size, iris exposure ratio) in the generated images. This disentanglement facilitates identity control over synthetic iris images. Since the iris has the multi-degree-of-freedom (MDOF) topology and high-entropy texture, we specially design the dual-channel input protocol to separate the topology and texture of the iris, so that the generator can infer multi-condition iris images while maintaining the unique texture details. Extensive experiments demonstrate that the proposed approach achieves impressive performance in both image quality and identity representation.