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In recent years, mobile Internet has accelerated the proliferation of smartmobile development. The mobile payment, mobile security and privacy protectionhave become the focus of widespread attention. Iris recognition becomes ahigh-security authentication technology in these fields, it is widely used indistinct science fields in biometric authentication fields. The ConvolutionalNeural Network (CNN) is one of the mainstream deep learning approaches forimage recognition, whereas its anti-noise ability is weak and needs a certainamount of memory to train in image classification tasks. Under these conditionswe put forward a fine-tuning neural network model based on the Mask R-CNN andInception V4 neural network model, which integrates every component in anoverall system that combines the iris detection, extraction, and recognitionfunction as an iris recognition system. The proposed framework has thecharacteristics of scalability and high availability; it not only can learnpart-whole relationships of the iris image but also enhancing the robustness ofthe whole framework. Importantly, the proposed model can be trained using thedifferent spectrum of samples, such as Visible Wavelength (VW) and NearInfrared (NIR) iris biometric databases. The recognition average accuracy of99.10% is achieved while executing in the mobile edge calculation device of theJetson Nano.

Learning scale-variant features for robust iris authentication with deep learning based ensemble framework