An efficient CNN based encrypted Iris recognition approach in cognitive-IoT system

Abstract

Recently, biometric-based security plays a vital role in the success of the Cognitive Internet of Things (C-IoT) based security framework. The iris trait solves a lot of security issues, especially in smart IoT-based applications. It increases the resistance of these systems against severe authentication attacks. In this paper, an efficient iris recognition model based on chaotic encryption and deep Convolutional Neural Networks (CNNs) is proposed for C-IoT applications. CNN is used to extract the deep iris features from the left and right eyes, which will be used as input features to a fully connected neural network with a Softmax classifier. CASIA V4 Interval dataset and Phoenix dataset are used to train the CNN model; to get the best tuning of network parameters. In this paper, the effect of adding different kinds of noise to iris images, due to noise interference related to sensing IoT devices, bad acquisition of iris images by system users, or other system assaults, is discussed. This strategy of noisy encrypted iris images is evaluated over the internet environment. Chaotic encryption is utilized to secure the transmission of iris templates in the proposed model. The results showed that the proposed approach attains supreme accuracy compared to the existing approaches, it is obtained up to 99.24% and 100% with CASIA V4 and Phoenix datasets, respectively. The proposed model achieves satisfied and competitive results regard accuracy, and robustness among existing methods. Regards to recognition accuracy rate, this methodology shows low degradation of recognition accuracy rates in the case of using noised iris images. Likewise, the proposed method has a relatively low training time, which is a useful parameter in critical IoT based uses such as Tele-Medicine application.