Knobbly kneed ID

March 25, 2009

Forget LED thumb-pad identification devices, complex retinal laser scanning, or even computerized iris recognition, the way forward for biometric validation is a quick X-ray snapshot of a person's knees, according to a report published in the International Journal of Biometrics.

Lior Shamir of the Laboratory of Genetics, National Institute on Aging, at the National Institutes of Health, and colleagues working with State University of New York at Farmingdale computer engineer Salim Rahimi explain that identification of individuals often requires focusing on unique features such as their face, fingerprints or retina. They explain that a similar identification process with countless applications in building security, at border crossings and elsewhere might equally use the unique nature of person's internal body parts, such as their knobbly knees.

Internal body parts are obviously invisible to the unaided eye but Shamir and colleagues have now demonstrated that knee X-rays can be used for identification purposes. The approach rapidly analyses the X-ray images using the wnd-charm algorithm, which has previously been used to diagnose clinical conditions of the knee joints.

The advantage of using a biometric identification process based on this kind of imaging is that it would be so much more difficult for a fraudster to spoof the knees or other internal body part in the way that they might with artificial fingerprints or contact lenses.

The team points out that the algorithm can correctly identify a given pair of knees and match it to a specific individual in the database even if the original X-ray were taken several years earlier. Identifiable features correspond to specific persons, rather than the present clinical condition of the joint, the researchers say.

The Wnd-charm algorithm, which is publicly available, is a multi-purpose image classification method that looks at a large set of image features, including high-contrast features, textures, and the statistical distribution of pixels in the image.

The team used a dataset of 1700 X-ray images from 425 individuals, representing four knee joint images per person in the dataset. They digitized the X-rays as 8 megapixel scans and characterized a central area of 700 × 500 pixels for the joint detection algorithm to process.

They found that accuracy levels were yet not as high as iris detection or fingerprint identification with the current algorithm but are much better than random results. The algorithm might now be refined to improve accuracy considerably and an alternative imaging process such as terahertz imaging might also offer more precise data.
-end-
"Biometric identification using knee X-rays" in Int. J. Biometrics, 2009, 1, 365

Inderscience Publishers

Related Algorithm Articles from Brightsurf:

CCNY & partners in quantum algorithm breakthrough
Researchers led by City College of New York physicist Pouyan Ghaemi report the development of a quantum algorithm with the potential to study a class of many-electron quantums system using quantum computers.

Machine learning algorithm could provide Soldiers feedback
A new machine learning algorithm, developed with Army funding, can isolate patterns in brain signals that relate to a specific behavior and then decode it, potentially providing Soldiers with behavioral-based feedback.

New algorithm predicts likelihood of acute kidney injury
In a recent study, a new algorithm outperformed the standard method for predicting which hospitalized patients will develop acute kidney injury.

New algorithm could unleash the power of quantum computers
A new algorithm that fast forwards simulations could bring greater use ability to current and near-term quantum computers, opening the way for applications to run past strict time limits that hamper many quantum calculations.

QUT algorithm could quash Twitter abuse of women
Online abuse targeting women, including threats of harm or sexual violence, has proliferated across all social media platforms but QUT researchers have developed a sophisticated statistical model to identify misogynistic content and help drum it out of the Twittersphere.

New learning algorithm should significantly expand the possible applications of AI
The e-prop learning method developed at Graz University of Technology forms the basis for drastically more energy-efficient hardware implementations of Artificial Intelligence.

Algorithm predicts risk for PTSD after traumatic injury
With high precision, a new algorithm predicts which patients treated for traumatic injuries in the emergency department will later develop posttraumatic stress disorder.

New algorithm uses artificial intelligence to help manage type 1 diabetes
Researchers and physicians at Oregon Health & Science University have designed a method to help people with type 1 diabetes better manage their glucose levels.

A new algorithm predicts the difficulty in fighting fire
The tool completes previous studies with new variables and could improve the ability to respond to forest fires.

New algorithm predicts optimal materials among all possible compounds
Skoltech researchers have offered a solution to the problem of searching for materials with required properties among all possible combinations of chemical elements.

Read More: Algorithm News and Algorithm Current Events
Brightsurf.com is a participant in the Amazon Services LLC Associates Program, an affiliate advertising program designed to provide a means for sites to earn advertising fees by advertising and linking to Amazon.com.