<p>To address the limitations of traditional Gabor filters—specifically their reliance on manual parameter adjustment and limited universality, an iris recognition algorithm based on adaptive Gabor filters and support vector machine (SVM) is proposed. Due to the unique grayscale distribution characteristics of the iris, the combination of adaptive thresholding and least squares methods is used for iris inner edge localization. Based on the characteristics of the center and outer edge of the pupil, an extended ray method is adopted for iris outer edge localization, followed by improvements in the sub-calculus operator to narrow down the search area, thus enhancing the efficiency of iris localization. An improved Particle Swarm Optimization (PSO) algorithm is employed to optimize the selection of Gabor filter parameters, enabling adaptive parameter selection for Gabor filters and completing feature encoding of iris images. By combining the nonlinear search capabilities of PSO and SVM, iris feature matching and recognition are effectively achieved. The improved algorithm demonstrated an increase in recognition rate by 0.59% and a decrease in equal error rate by 0.48% on the CASIA 1.0 iris database. Similarly, on the Lamp iris database, the recognition rate improved by 0.3%, and the equal error rate decreased by 0.34%. Compared to traditional iris recognition methods, the improved PSO algorithm is employed to automatically optimize Gabor filter parameter selection. This approach overcomes the manual parameter tuning challenges in traditional methods across different iris databases. This significantly enhances the recognition performance of the system, making it more practical and robust.</p>