Palmprint Image Recognition Using Gabor Filters

In this research, we implement a palmprint recognition system to identify palmprint images using advanced pattern recognition techniques. Our methodology employs Gabor filters to enhance fine-grained details in palmprint images, significantly improving recognition accuracy through multi-scale and multi-orientation feature extraction. The implementation involves convolving palmprint images with banks of Gabor filters at different frequencies and orientations to capture distinctive texture patterns. We conducted comprehensive experiments to validate the effectiveness and reliability of our approach, comparing performance against traditional methods. Experimental results demonstrate that our Gabor filter-based method achieves superior accuracy and robustness compared to conventional techniques. Furthermore, we systematically investigated the impact of various Gabor filter parameters (including wavelength, orientation, phase offset, and bandwidth) on recognition performance. Through parameter tuning and optimization algorithms, we identified the optimal parameter combination that maximizes recognition performance, achieving the highest identification rates in our testing scenarios.