Palmprint recognition is a promising biometric feature for use in biometric access control and forensic applications. Palmprint recognition system works much like a fingerprint recognition system except a few additional features. Palmprint recognition system is a digital image processing system which can be designed using a DSP processor and a MATLAB sourcecode written C-language. Optionally the Palmprint recognition system can be implemented VLSI FPGA hardware using Altera/Xilinx Matlab-FPGA toolboxes.

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.

Palm print recognition inherently implements many of the same matching characteristics that have allowed fingerprint recognition to be one of the most well-known and best publicized biometrics. Both palm and finger biometrics are represented by the information presented in a friction ridge impression. This information combines ridge flow, ridge characteristics, and ridge structure of the raised portion of the epidermis.

Palmprint recognition system functions by projecting palmprint images onto a feature space that spans the significant variations among known images.

Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted.