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Delhi Police Uses Facial Recognition Software To Catch Rioters After Filming Them During Anti-CAA Protests

Swarajya Staff

Dec 30, 2019, 05:39 PM | Updated 05:39 PM IST


A still from the anti CAB protests that turned violent in Seelampur Delhi (Representative image) (Source: Twitter)
A still from the anti CAB protests that turned violent in Seelampur Delhi (Representative image) (Source: Twitter)

Delhi Police used a facial recognition software to screen the crowd during Prime Minister Narendra Modi’s Ramlila Maidan rally on 22 December, reports The Indian Express.

Police was using facial images collected from footage filmed at the city’s various protest events to filter “law and order suspects” at the Prime Minister’s rally.

The facial recognition software was acquired after a Delhi High Court order in a case related to missing children and the Automated Facial Recognition System (AFRS) software was put to use from March 2018 to identify lost and found boys and girls by matching photos.

AFRS has also been used during Independence Day parade and the Republic Day parade.

According to sources Delhi Police has created a photo dataset of over 1.5 lakh “history-sheeters” for routine crime investigation and over 2000 images of terror suspects and alleged “rabble-rousers and miscreants”.

Police could be seen videotaping protests and events where public property was damaged or destroyed and the footage is being fed to AFRS which extracts “identifiable faces” of the protesters to its dataset and the system is capable of filtering and identify and retain “habitual protesters” and “rowdy elements”.

When asked what checks and balances were built in to avert misuse of images and satisfy privacy concerns, the spokesperson for Delhi Police said, “Without getting into operational details, Delhi Police assures that best industry standard checks and balances against any potential misuse of data are in place. Such datasets are not perpetual and are revised periodically. Racial or religious profiling is never a relevant parameter while building these datasets.”


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