  In recent years, mobile Internet has accelerated the proliferation of smart
mobile development. The mobile payment, mobile security and privacy protection
have become the focus of widespread attention. Iris recognition becomes a
high-security authentication technology in these fields, it is widely used in
distinct science fields in biometric authentication fields. The Convolutional
Neural Network (CNN) is one of the mainstream deep learning approaches for
image recognition, whereas its anti-noise ability is weak and needs a certain
amount of memory to train in image classification tasks. Under these conditions
we put forward a fine-tuning neural network model based on the Mask R-CNN and
Inception V4 neural network model, which integrates every component in an
overall system that combines the iris detection, extraction, and recognition
function as an iris recognition system. The proposed framework has the
characteristics of scalability and high availability; it not only can learn
part-whole relationships of the iris image but also enhancing the robustness of
the whole framework. Importantly, the proposed model can be trained using the
different spectrum of samples, such as Visible Wavelength (VW) and Near
Infrared (NIR) iris biometric databases. The recognition average accuracy of
99.10% is achieved while executing in the mobile edge calculation device of the
Jetson Nano.
