Deep Periocular Recognition Method via Multi-Angle Data Augmentation

Abstract Periocular recognition technology is a biometric recognition technology widely used in identity verification. Because of its high precision, high ease of use and high security, Periocular recognition has a broad application prospect and scientific research value. In order to solve the problem of angular rotation of eyes in practical application, this paper proposes a deep learning periocular recognition method based on multi-angle data augmentation. The method is to rotate the original data set from small angle to large angle, so that the amount of data is expanded to 7 times of the original, and the diversity of data is increased at the same time. The InceptionV3 network and MobileNetV2 lightweight network are used for experimental verification respectively, and good results are obtained from multi-angle tests, indicating that the proposed method can improve the generalization ability of the model and has good robustness.