Machine learning identifies terrorists's photo poses

Scientists in Jordan have developed a biometric measuring system using AI to identify terrorists based on the V signs they use in photos. Analysing the end points of two fingers and the lowest point of the V shape, and two points in the palm, the team used two-thirds of the images to train a machine-learning algorithm to recognise different V signs with an accuracy rate of over 90% in some cases.

Today, Ahmad Hassanat at Mu'tah University in Jordan and a few pals say they have found just such a method. These guys say they have worked out how to distinguish people from the unique way they make V signs; finger size and the angle between the fingers is a useful biometric measure like a fingerprint.

The idea of using hand geometry as a biometric indicator is far from new. Many anatomists have recognized that hand shape varies widely between individuals and provides a way to identify them, if the details can be measured accurately.

However, the task of recognizing people using just a section of their hands is much harder. “Identifying a person using a small part of the hand is a challenging task, and has, to the best of our knowledge, never been investigated,” say Hassanat and co.

The team began by asking 50 men and women of varying ages to make a V sign with their right hand and photographing it several times against a black background with an eight-megapixel camera phone. This produced a database of 500 images.

An important question is how much information should be extracted from these images to aid in identification. Hassanat and co point out that many real-word images have low resolution, which limits the amount of information that can be gathered.

So they limited their analysis to determining the end points of the two fingers, the lowest point in the valley in between them, and two points in the palm of the hand.

This allowed them to analyze various triangle shapes between these points, their relative size and the angles they make, and so on.

They also used a second method to analyze the shape of the hand using a number of statistical measures. Combining these two methods creates a total of 16 different features that can be used in identification.