From July 2025, the government made facial recognition mandatory for anganwadi beneficiaries via the Poshan Tracker app.

• Though the move aimed at curbing leakage, there was a risk of increased workers Burden and exclusion.

Facial Recognition in Anganwadi Services

• Background: In 2021, the Union government launched the Poshan Tracker, a centralised application platform, to monitor nutrition initiatives. 
  • The Anganwadi worker (AWW) has to install the Poshan tracker app on her smartphone and periodically upload the nutritional status of children. 

• Implementation Requirement: Beneficiaries need to authenticate their faces through the Facial Recognition Software (FRS), which is now integrated with this app otherwise Take Home Rations (THR) for pregnant/lactating women will not be given
  • To get to this stage: e-KYC has to be first completed, where the woman’s Aadhaar and biometric details are entered and verified by OTP. 
• Purpose: 
  • A child or woman is not faking to be somebody else to get food. 
  • Child’s food is not stolen by the Anganwadi worker (AWW) or anyone. 

Challenges

• Distortion of Welfare Delivery: Time and resources are diverted to digital authentication, reducing focus on service outcomes like nutrition, education, or healthcare.
  • Example: Anganwadi workers spend hours authenticating beneficiaries on the Poshan Tracker app, leaving less time for preschool education and health monitoring.
  • Example: MGNREGA workers face delays due to mandatory NREGA Mobile Monitoring Service (NMMS) app-based attendance, reducing focus on actual work.
• Technical Glitches and Infrastructure Deficits: App crashes, poor internet, outdated devices, and lack of offline functionality undermine reliability.
  • Example: In rural Assam, Anganwadi workers protested against FRS citing poor connectivity and repeated failures.
  • Example: PDS shops in Jharkhand reported Aadhaar biometric mismatches due to weak connectivity, denying rations to genuine families.
• Exclusion of Genuine Beneficiaries: Machine failures (face mismatches, fingerprint errors) directly deny access to welfare.
  • Example: Children’s changing facial features confuse FRS in Anganwadis, forcing mothers to make repeated visits and lose wages.
• Capacity and Training Gaps: Frontline workers are given limited digital training, lack troubleshooting skills, and are not compensated for extra workload created by machine failures.
  • Example: Many Anganwadi workers rely on their children to operate the Poshan Tracker app when it crashes.
• Rights Reduced to Machine Approval: Welfare entitlements shift from being unconditional rights under laws like NFSA and MGNREGA to conditional benefits dependent on successful machine recognition.
  • Example: Even when Anganwadi workers personally know all beneficiaries, they cannot distribute rations if the app refuses authentication.
• Dehumanisation of Welfare: Vulnerable groups are treated as suspects rather than citizens, undermining dignity and trust.
  • Example: FRS, typically used in criminal investigations, is now used on women and children in Anganwadis.
  • Example: Aadhaar-based biometric authentication in PDS criminalises beneficiaries by assuming fraud unless verified by machine.
• Misplaced Priorities: Technology is deployed to solve marginal issues like “fake beneficiaries,” while core problems in welfare like poor ration quality, irregular supply, stagnant budgets (₹8 per child/day in THR since 2018), and corruption in contracts  remain unaddressed.
  • Example: In Anganwadis, FRS has been prioritised over improving nutrition quality and decentralising ration supply through SHGs, despite Supreme Court orders.
• Violation of Natural Justice: By presuming workers and beneficiaries guilty until machines prove innocence, welfare delivery undermines the principle of innocent until proven guilty.
  • Example: Anganwadi workers cannot override FRS failure even when they personally verify the authenticity of beneficiaries.

Way Forward

• User-Centric Design: Build welfare technology to simplify access, not complicate it.
  • Ensure flexibility and ease of use for both beneficiaries and workers.
• Hybrid Verification Systems: Allow community verification and manual overrides when machines fail.
  • Trust Anganwadi workers’ knowledge of local beneficiaries.
• Strengthen Core Service Delivery: Improve ration quality, increase budget allocations, and ensure regular supply.
  • Implement SC orders on decentralised production via SHGs and women’s groups.
• Worker Support and Training: Provide better training, functional devices, and compensation for increased workload.
• Transparency and Consultation: Publish evidence of fraud if it exists.
  • Involve frontline workers and community stakeholders before rolling out technology.
• Protect Dignity and Rights: Treat women and children as rights-holders, not suspects.
  • Keep welfare entitlements unconditional and independent of machine efficiency.
    

Conclusion

• While integrating technology like facial recognition in welfare schemes can help improve accountability and reduce fraud, it must not come at the cost of excluding those who most need support. Welfare programmes should not depend solely on how well a machine performs.

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