Efficient Palm Image Preprocessing for Person Identification and Security System Using Machine Learning Approaches

Due to its non-intrusive aspect and distinctive biometric features, palm print identification technology has attracted a lot of attention recently and is now a crucial part of contemporary security mechanisms. This study explores how to improve palm print image preprocessing methods for security systems using an entirely novel approach called Receiver Operating Characteristic (ROC) assessment. Furthermore, it investigates the extraction of attributes using three well-known techniques: Scale-Invariant Feature Transform (SIFT), Local Binary Patterns (LBP), and Speeded-Up Robust Features (SURF). This study, which highlights its improved performance in terms of Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Silhouette Score, is important for looking at the collaborative influence of ROC analysis in combination with SURF. From the result obtained we can prove that SURF produces MSE of 0.00248, RMSE of 0.05850, Silhouette Score of 0.6, SSIM of 0.998 and PSNR of 42.35respectively which is better than other algorithms. The tool used for execution is Jupyter Notebook and the language used is python.