Explainer: Palm print recognition

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 over 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.

Palm identification, just like fingerprint identification, is based on the aggregate of information presented in a friction ridge impression. This information includes the flow of the friction ridges (Level 1 Detail), the presence or absence of features along the individual friction ridge paths and their sequences (Level 2 Detail), and the intricate detail of a single ridge (Level 3 detail).

To understand this recognition concept, one must first understand the physiology of the ridges and valleys of a fingerprint or palm. When recorded, a fingerprint or palm print appears as a series of dark lines and represents the high, peaking portion of the friction ridged skin while the valley between these ridges appears as a white space and is the low, shallow portion of the friction ridged skin.

Palm recognition technology exploits some of these palm features. Friction ridges do not always flow continuously throughout a pattern and often result in specific characteristics such as ending ridges or dividing ridges and dots. A palm recognition system is designed to interpret the flow of the overall ridges to assign a classification and then extract the minutiae detail — a subset of the total amount of information available, yet enough information to effectively search a large repository of palm prints. Minutiae are limited to the location, direction, and orientation of the ridge endings and bifurcations (splits) along a ridge path.

A variety of sensor types: capacitive, optical, ultrasound and thermal, can be used for collecting the digital image of a palm surface; however, traditional live-scan methodologies have been slow to adapt to the larger capture areas required for digitizing palm prints. Challenges for sensors attempting to attain high-resolution palm images are still being dealt with today. One of the most common approaches, which employs the capacitive sensor, determines each pixel value based on the capacitance measured, made possible because an area of air (valley) has significantly less capacitance than an area of palm (ridge). Other palm sensors capture images by employing high frequency ultrasound or optical devices that use prisms to detect the change in light reflectance related to the palm. Thermal scanners require a swipe of a palm across a surface to measure the difference in temperature over time to create a digital image. Capacitive, optical, and ultrasound sensors require only placement of a palm.

Some palm recognition systems scan the entire palm, while others require the palms to be segmented into smaller areas to optimize performance. Maximizing reliability within either a fingerprint or palm print system can be greatly improved by searching smaller data sets. While fingerprint systems often partition repositories based upon finger number or pattern classification, palm systems partition their repositories based upon the location of a friction ridge area. Latent examiners are very skilled in recognizing the portion of the hand from which a piece of evidence or latent lift has been acquired. Searching only this region of a palm repository rather than the entire database maximizes the reliability of a latent palm search.

Like fingerprints, the three main categories of palm matching techniques are minutiae-based matching, correlation-based matching, and ridge-based matching. Minutiae-based matching, the most widely used technique, relies on the minutiae points described above, specifically the location, direction, and orientation of each point. Correlation-based matching involves simply lining up the palm images and subtracting them to determine if the ridges in the two palm images correspond. Ridge-based matching uses ridge pattern landmark features such as sweat pores, spatial attributes, and geometric characteristics of the ridges, and/or local texture analysis, all of which are alternates to minutiae characteristic extraction. This method is a faster method of matching and overcomes some of the difficulties associated with extracting minutiae from poor quality images.

The advantages and disadvantages of each approach vary based on the algorithm used and the sensor implemented. Minutiae-based matching typically attains higher recognition accuracy, although it performs poorly with low quality images and does not take advantage of textural or visual features of the palm.

Processing using minutiae-based techniques may also be time consuming because of the time associated with minutiae extraction. Correlation-based matching is often quicker to process but is less tolerant to elastic, rotational, and translational variances and noise within the image. Some ridge-based matching characteristics are unstable or require a high-resolution sensor to obtain quality images. The distinctiveness of the ridge-based characteristics is significantly lower than the minutiae characteristics.

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. Interoperability is a crucial aspect of product implementation, meaning that images obtained by one device must be capable of being interpreted by a computer using another device. Major standards efforts for palm prints currently underway are the revision to the ANSI NIST ITL-2000 Type-15.

Many, if not all, commercial palm AFIS systems comply with the ANSI NIST ITL-2000 Type-15 record for storing palm print data. Several recommendations to enhance the record type are currently being “vetted” through workshops facilitated by the National Institute for Standards and Technology. Specifically, enhancements to allow the proper encoding and storage of Major Case Prints, essentially any and all friction ridge data located on the hand, are being endorsed to support the National Palm Print Service initiative of the FBI’s NGI.

Unlike several other biometric applications, a large-scale U.S. government sponsored evaluation has not been performed for palm recognition. The amount of data currently available for test purposes has hindered the ability for not only the federal government but also the vendors in efficiently testing and benchmarking commercial palm systems. The FBI Laboratory is currently encoding its hard-copy records into three of the most popular commercial palm recognition systems. This activity, along with other parallel activities needed for establishing a National Palm Print Service, will address these limitations and potentially provide benchmark data for U.S. government evaluations of palm systems.

Sources: NIST, FBI

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