NIR Palm Vein Pattern Recognition

The 850nm near-infrared (NIR) palm vein pattern imageries are considered and have been successfully recognized utilizing image processing in machine learning. The proposed method has well combined the ROI extraction, feature extraction to stipulate the desired response. The palm veins imageries are enhanced and denoised using a median filter. The Otsu Binarization is applied and finally, the 850nm NIR palm vein pattern collective image-enabled dataset has been successfully recognized. Thus, paper exhibits a novel near-infrared palm vein pattern recognition for personal authorization. The very famous CASIA database is used to introduce to recognize the palm vein pattern with a local binary pattern-based method. Various palm vein features extraction and correction are done to establish the quantification between the test data and the trained data. Hence, amalgamating the power of image processing in machine learning and Python programming, the proposed novel palm vein pattern for personal recognition method is designed which provides a user-friendly environment.