A Fractional-Order Chaotic Sparrow Search Algorithm for Enhancement of Long Distance Iris Image

Abstract

At present, iris recognition has been widely used as a biometrics-based security enhancement technology. However, in some application scenarios where a long-distance camera is used, due to the limitations of equipment and environment, the collected iris images cannot achieve the ideal image quality for recognition. To solve this problem, we proposed a modified sparrow search algorithm (SSA) called chaotic pareto sparrow search algorithm (CPSSA) in this paper. First, fractional-order chaos is introduced to enhance the diversity of the population of sparrows. Second, we introduce the Pareto distribution to modify the positions of finders and scroungers in the SSA. These can not only ensure global convergence, but also effectively avoid the local optimum issue. Third, based on the traditional contrast limited adaptive histogram equalization (CLAHE) method, CPSSA is used to find the best clipping limit value to limit the contrast. The standard deviation, edge content, and entropy are introduced into the fitness function to evaluate the enhancement effect of the iris image. The clipping values vary with the pictures, which can produce a better enhancement effect. The simulation results based on the 12 benchmark functions show that the proposed CPSSA is superior to the traditional SSA, particle swarm optimization algorithm (PSO), and artificial bee colony algorithm (ABC). Finally, CPSSA is applied to enhance the long-distance iris images to demonstrate its robustness. Experiment results show that CPSSA is more efficient for practical engineering applications. It can significantly improve the image contrast, enrich the image details, and improve the accuracy of iris recognition.

Recent Versions and Applications of Sparrow Search Algorithm

“…Xiong et al [ 74 ] introduced another chaotic SSA for enhancement of long-distance iris image, called CPSSA. First, fractional-order chaos was utilized for generating the initial population to enhance the population diversity.…”

“…The SSA is also used to assess the BP neural networks to address the imaging deviation prediction [ 114 ]. The long-distance iris image is enhanced using a chaotic version of SSA [ 74 ]. Finally, the Camera calibration is optimized using the original version of SSA [ 86 ].…”

Parameter Identification for Memristive Chaotic System Using Modified Sparrow Search Algorithm

“…To avoid the local minimum points, the Pareto distribution is utilized [25]. As a result, the scroungers' location update formula is changed by Eq.…”

“…Table 2 contains the best value, average, standard deviation, and computation time. The best value reflects exploration ability, the average value demonstrates convergence accuracy, and the standard deviation depicts the SPSSA's stability under the same benchmark test function [25].…”

“…To avoid this phenomenon, many researchers proposed some improved methods. For example, Xiong et al [25] used a fractional-order chaotic sequence to increases the diversity of the population and used Pareto mutation to escape local best. These methods achieve better results than traditional SSA on 12 benchmark functions.…”