Keeping in view the safety of women, the government is taking tough steps and making every effort. Recently, the Central Government has informed the Supreme Court that it is planning to install Artificial Intelligence (AI) based facial recognition system at seven major railway stations including Mumbai CST and New Delhi to curb crimes against women. The central government has told the court that the number of sexual criminals registered in the National Database (NDSO) on sex criminals has crossed the 20 lakh mark. In such a situation, there is a need to tighten them.

This system will be applicable in which cities

It will include seven railway stations in the first phase, including Mumbai Chhatrapati Shivaji Terminal, New Delhi. Soon the names of other stations can also be revealed. Earlier, security marsers have been started in eight cities under the Safety City Project.

How will AI system work

According to the Ministry of Home Affairs, such technology is being used, including facial recognition camera, automatic number plate reader (ANPR), smart street lighting, monitoring and CCTV surveillance through drones. The Ministry said, the Integrated Emergency Response Management System (IERMS) has been started at 499 out of 983 major railway stations, ensuring 24×7 security for women passengers. In the next phase, the AI -based face recognition system will be set up.

What does crime report say?

The Ministry of Home Affairs says that the National Sexual Criminal Database (NDSO) includes the names, photos and fingerprints of the people involved in rape, tampering, gangrape, chase and misconduct with children. This includes 20.28 lakh information so far, which can be used by all police stations and law-enforcement agencies through the Inter-Operational Criminal Justice System (ICJS).

According to the data of the National Crime Bureau, the crime against women has increased from 58.8 per lakh in 2018 to 2022 per lakh 66.4. At the same time, 23.66 lakh cases were pending in the court in 2022, but only 38,136 cases have been punished.

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