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

• Published in: Mathematics (Basel) Vol. 9; no. 21; p. 2790
• Main Authors: Xiong, Qi, Zhang, Xinman, He, Shaobo, Shen, Jun
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2021
• Subjects: Accuracy; Biometric identification; Biometric recognition systems; Biometrics; contrast limited adaptive histogram equalization (CLAHE); Equalization; fractional-order chaos; Histograms; Image contrast; Image enhancement; Image quality; iris images; Mathematics; Noise; Normal distribution; Object recognition; Optimization algorithms; Particle swarm optimization; Population; Search algorithms; sparrow search algorithm; swarm intelligence
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math9212790

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.