Palm Print

Palm Print palmprint.gif


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. The data represented by these friction ridge impressions allows a determination that corresponding areas of friction ridge impressions either originated from the same source or could not have been made by the same source. Because fingerprints and palms have both uniqueness and permanence, they have been used for more than a century as a trusted form of identification. However, palm recognition has been slower in becoming automated due to some restraints in computing capabilities and live-scan technologies.


In many instances throughout history, examination of handprints was the only method of distinguishing one illiterate person from another since they could not write their names. More information (pdf)

Approach Concept

Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression. More information (pdf)


A variety of sensor types—capacitive, optical, ultrasound, and thermal—can be used for collecting the digital image of a palm surface. More information (pdf)


Some palm recognition systems scan the entire palm, while others require the palms to be segmented into smaller areas to optimize performance. More information (pdf)

United States Government Evaluations

Unlike several other biometrics, a large-scale government-sponsored evaluation has not been performed for palm recognition. More information (pdf)

Standards Overview

Just as with fingerprints, standards development is an essential element in palm recognition because of the vast variety of algorithms and sensors available on the market. More information (pdf)


Even though total error rates are decreasing when comparing live-scan enrollment data with live-scan verification data, improvements in matches between live-scan and latent-print data are still needed. Data indicates that fully integrated palm- and fingerprint multi-biometric systems are widely used for identification and verification of criminal subjects as well as in security access applications. However, there are still significant challenges in balancing accuracy with system cost. Image matching accuracy may be improved by building and using larger databases and by employing more processing power, but then purchase and maintenance costs will most certainly rise as the systems become larger and more sophisticated. Future challenges require balancing the need for more processing power with more improvement in algorithm technology to produce affordable systems to all levels of law enforcement.

Note: The text above is excerpted from