Machine-Learning Algorithm Aims to Identify Terrorists Using the V Signs They Make
From the post:
Every age has its iconic images. One of the more terrifying ones of the 21st century is the image of a man in desert or army fatigues making a “V for victory” sign with raised arm while standing over the decapitated body of a Western victim. In most of these images, the perpetrator’s face and head are covered with a scarf or hood to hide his identity.

That has forced military and law enforcement agencies to identify these individuals in other ways, such as with voice identification. This is not always easy or straightforward, so there is significant interest in finding new ways.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. (emphasis in original)
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The review notes this won’t give you personal identity but would have to be combined with other data.
Overview of: Victory Sign Biometric for Terrorists Identification by Ahmad B. A. Hassanata, Mahmoud B. Alhasanat, Mohammad Ali Abbadi, Eman Btoush, Mouhammd Al-Awadi.
Abstract:
Covering the face and all body parts, sometimes the only evidence to identify a person is their hand geometry, and not the whole hand- only two fingers (the index and the middle fingers) while showing the victory sign, as seen in many terrorists videos. This paper investigates for the first time a new way to identify persons, particularly (terrorists) from their victory sign. We have created a new database in this regard using a mobile phone camera, imaging the victory signs of 50 different persons over two sessions. Simple measurements for the fingers, in addition to the Hu Moments for the areas of the fingers were used to extract the geometric features of the shown part of the hand shown after segmentation. The experimental results using the KNN classifier were encouraging for most of the recorded persons; with about 40% to 93% total identification accuracy, depending on the features, distance metric and K used.
All of which makes me suspect that giving a surveillance camera the “finger,” indeed, your height, gait, any physical mannerism, are fodder for surveillance systems.
Hotels and businesses need to construct privacy zones for customers to arrive and depart free from surveillance.