Centre introduces AI-based verification upgrade in attendance app for rural jobs scheme

You don’t have any Active Subscription.

Subscribed with another email? Logout and Login with that one.

Your active subscription(s)

Account subscription benefits alongside Premium Stories, Editorials, Opinions and more. Unlock these with Subscription

Products you've access to

Additional Subscription Benefits

Need help with your subscription?

Updated - October 23, 2026 09:59 pm IST - New Delhi

Sharing the progress of the scheme that was launched on July 1, the Minister said currently 31.23 lakh worksites are active and employment had been provided to more than 3.65 crore workers belonging to 2.89 crore rural households. File | Photo Credit: Arun Kulkarni

With an aim to plug the gaps that allegedly allowed misuse of the mobile-based application used to capture workers’ attendance under the rural employment programme, Viksit Bharat-Guarantee for Rozgar and Ajeevika Mission (VB-G-RAM-G), Union Rural Development Minister Shivraj Singh Chouhan on Friday (October 23, 2026) announced that an upgraded version with AI-based verification and other enhanced security features that safeguard against tampering and fraudulent attendance, will be used.

Mr. Chouhan, according to a statement issued by the Press Information Bureau (PIB), said the government’s priority while ensuring employment for rural workers was to “protect their wages and ensure that public funds were used transparently and for their intended purpose”.

Face authentication was introduced in March 2026 as the Ministry’s response to concerns over fake attendance, under MGNREGA, that preceded the current rural programme.

However, despite the face authentication provision, in many cases “photo-to-photo capturing instead of live work images” was being done. There were also mismatches between the actual and recorded worker count. Discrepancies in the gender composition of workers were found at several worksites. Cases of the same photograph of workers appearing in multiple muster rolls, as well as mismatches between workers in morning and afternoon photographs, were also detected.

The latest version, Mr. Chouhan said, includes safeguards against cloning and tampering, along with liveness detection. “These features are designed to verify that the person appearing before the camera is a real, live worker rather than a dummy image or an old photograph. AI-based face verification has also been refined to account for differences across States and geographical regions,” he said.

Apart from this, the government has also put in place a “multi-layered verification system” to ensure that payments correspond to genuine work. The system includes an AI-based photo de-duplication module that identifies suspicious or repeated photographs. The Minister explained that where such entries are detected, the wage list and Fund Transfer Order (FTO) are automatically withheld until the necessary checks are completed.

Mr. Chouhan also clarified that in areas where network connectivity is unavailable at a worksite, attendance can be recorded offline. An authorised exception mechanism is also available when face authentication fails for elderly workers or persons with disabilities, or when a mobile device encounters technical problems.

Sharing the progress of the scheme that was launched on July 1, the Minister said currently 31.23 lakh worksites are active and employment had been provided to more than 3.65 crore workers belonging to 2.89 crore rural households. Nearly 46 crore person-days have been generated so far, of which approximately 40 crore have been recorded through face authentication, he said.

Published - October 23, 2026 09:55 pm IST

Comments have to be in English, and in full sentences. They cannot be abusive or personal. Please abide by our community guidelines for posting your comments.

We have migrated to a new commenting platform. If you are already a registered user of The Hindu and logged in, you may continue to engage with our articles. If you do not have an account please register and login to post comments. Users can access their older comments by logging into their accounts on Vuukle.