In the field of biometrics, palmprint is a novel but promising technology. Limited work has been reported on palmprint identification and verification, despite the importance of palmprint features. There are many unique features in a palmprint image that can be used for personal identification. Principal lines, wrinkles, ridges, minutiae points, singular points, and texture are regarded as useful features for palmprint representation.
The system functions by projecting palmprint images onto a feature space that spans the significant variations among known images. The significant features are known as "eigenpalms" because they are the eigenvectors (principal components) of the set of palmprints.
The images included are taken from CASIA Palmprint Database, available athttp://www.cbsr.ia.ac.cn/english/Palmprint%20Databases.asp. CASIA Palmprint Database contains 5239 palmprint images captured from 301 different people. All palmprint images are 8 bit gray-level JPEG files. We have developed a simple, fast and accurate scheme for automatic palmprint segmentation. Our approach can detect the region of interest in palmprint image where most of useful information is located. A robust image coordinate system is defined to facilitate image alignment for feature extraction.
Index Terms: Matlab, source, code, online palmprint identification, texture analysis, low-resolution image, palmprint recognition.
 | Figure 1. Palmprint image | ||
A simple and effective source code for Palmprint Recognition | |||
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