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.
Highlights
IntroductionBiometric identification has became an important approach to protect information security
We further propose an innovative modified sparrow search algorithm named chaotic pareto sparrow search algorithm (CPSSA) to enhance the long-distance iris image
In order to verify the performance of CPSSA, we have carried out the benchmark function comparison experiments, long-distance iris image enhancement experiments, and the respective comparison experiments
Summary
Biometric identification has became an important approach to protect information security. It uses the human body’s biologic characteristics such as iris, face, vein, and fingerprint recognition. Low-quality iris images have low contrast, unclear details, and some noise. These are inferior for identity recognition due to the loss of some texture information [3]. The iris edge and pupil center may not be accurately located. This will lead to iris segmentation errors, bring difficulties to subsequent image analysis and affect the accuracy of iris recognition [4]. It needs to enhance the visual clarity, and requires the enhanced image to have good performance in detection and recognition [5]
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