Abstract

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Iris recognition has become a popular research in recent years due to its reliability and nearly perfect recognition rates. Iris recognition system has three main stages: image preprocessing, feature extraction and template matching. In the preprocessing stage, iris segmentation is critical to the success of subsequent feature extraction and template matching stages. If the iris region is not correctly segmented, the eyelids, eyelashes, reflection and pupil noises would present in the normalized iris region. The presence of noises will directly deteriorate the iris recognition accuracy. The proposed approach gives a solution for compensating all four types of noises to achieve higher accuracy rate. It consists of four parts: (a) Pupil is localized using thresholding and Circular Hough Transform methods. Experimental results show that the proposed approach has achieved high accuracy of 98.62%.

SummaryIndustrial Internet of Things (IIoT) is an emerging technology that relies on the use of massively connected sensor nodes to gather industrial‐related data. The collected data are used for postanalysis to generate insights for reducing production down time, cost optimization, and predictive maintenance. One of the key requirements for sensor node in such application is the data confidentiality; as such sensor data may potentially leak the manufacturing and industrial secret to their competitors. In this paper, a field programmable gate array (FPGA)‐based sensor node with Advanced Encryption Standard (AES) crypto‐processor is proposed to safeguard the sensor data. A novel queue system is proposed to further reduce the data processing time and energy consumption. The proposed queue system is able to achieve 1.48× speed up and ∼16% energy reduction, which makes it a competitive candidate for Industrial IoT applications. The technique developed in this paper can also be extended to implement FPGA‐based gateway with encryption feature, which is very useful for edge computing in IoT applications.

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