Facial recognition tool used to identify Red Fort raiders

NEW DELHI: The Delhi Police have identified over 250 suspects, allegedly involved in the violent clashes during the farmers’ tractor rally on January 26, by using facial recognition software. The police have also used voter identity cards and driving licence details to zero in on two dozen miscreants who took part in the vandalism and forced their way into the Red Fort to hoist religious flags, officials said.

The Delhi Police have so far arrested 19 persons and detained over 200 persons after 33 FIRs were registered under various sections of IPC, Disaster Management Act, Epidemic Disease Act, UAPA and Prevention of Damage to Public Property Act, as per a statement. Nine FIRs have been transferred to SIT, including the crime branch and special cell. The DDA ground in Burari is being cleared of farmers, who are moving towards Singhu border.

On Thursday, the Delhi Police said it issued look-out circulars (LOCs) against 44 farmers’ leaders. They will be asked to surrender their passports so that they cannot leave the country while the probe is underway.

This decision was taken on the directions of home minister Amit Shah, who has been monitoring the situation in Delhi, they added. Once issued, the LOC is valid for a year unless extended or withdrawn by the investigating agency. It prevents a person from travelling by sea, land or air routes. The immigration authorities will use the passport details of farmers to prevent them from travelling outside the country, the officials said.

As many as 38 farmer leaders including Darshan Pal, Swaraj Abhiyan’s Yogendra Yadav, Rajinder Singh, Balbir Singh Rajewal, Buta Singh Burjgil and Joginder Singh Ugraha and Bharatiya Kisan Union (BKU) spokesperson Rakesh Tikait have been named for allegedly violating the conditions laid down for the tractor rally.

In one of the FIRs, Punjabi film actor Deep Sidhu and Lakhbir Singh Sidhana alias Lakha Sidhana, gangster-turned-politician and president of the Malwa Youth Federation, have been named as accused.