CASIA-PR-V1: A multi-ethnic, multi-device and cross-spectral dataset and a multiscale disentangled model for periocular recognition

Periocular recognition is regarded as an alternative trait for biometric recognition that can effectively solve the identification problem under large occlusions. However, few datasets are tailored for periocular recognition. For most compromises, iris datasets at near-infrared wavelengths, miss information about the eyebrows or eyelids. In this paper, a challenging dataset for real scenarios named CASIA-PR-V1 with evaluation protocols is released for periocular recognition. It is collected from multiple types of mobile devices with different resolutions or wavelengths. A rich set of attributes, e.g., ethnicities, is tagged to support fine-grained classification tasks. Moreover, we consider a wide range of noisy data in unconstrained environment, especially for glasses. Superior to its counterparts, this periocular dataset is highly valuable for studying cross-device and cross-spectral periocular recognition with occlusions, as well as fine-grained attribute classification. Additionally, a multiscale disentangled model is proposed to extract discriminating representations for periocular recognition with severe occlusions. Extensive experiments are conducted on CASIA-PR-V1, and the results indicate the superiority of our model for unconstraint periocular recognition. Please visit http://biometrics.idealtest.org for more details about our dataset.