Facial Recognition System developed in Poshan Tracker Application for last mile tracking of Take-Home Ration distribution

1. At a Glance

2. Why in the News

3. Background & Evolution

4. Core Static Facts

5. Multi-Dimensional Analysis

Administrative / Governance - Operationalises DBT-style targeting for in-kind nutrition transfers; removes ghost & duplicate entries via biometric de-duplication [S1]. - Centre–State implementation: MoWCD develops the stack; Anganwadi Workers (AWWs) at State level operate it; NeGD provides capacity-building [S1].

Social / Equity - Critical for PW, LM, adolescent girls and under-6 children — categories central to reducing IMR, MMR, stunting (NFHS-5 indicators) [S2]. - Nominee Module mitigates exclusion error where a beneficiary cannot present herself (illness, migration, purdah) [S2].

Scientific / Technological - Layered authentication: Aadhaar e-KYC + facial biometrics on a mobile device, integrating with Poshan Tracker's MIS [S1]. - Example of Digital Public Infrastructure (DPI) stacking — Aadhaar + DigiLocker-style verification + scheme MIS.

Ethical / Legal - Biometric capture of women and children raises Puttaswamy (2017) privacy-test concerns: legality, necessity, proportionality. - Digital Personal Data Protection Act, 2023 obligations (purpose limitation, consent for minors via guardian) directly apply. - Risk of exclusion if face match fails in poor-network/dark-skin/age-related conditions — equity vs. efficiency trade-off.

Economic - Plugging leakage in THR (procurement worth several thousand crore annually) directly improves fiscal efficiency of nutrition spend under the 15th FC cycle.

6. Recent Developments (last 12-18 months)

7. Prelims Hooks

8. Mains Relevance

9. Related Topics to Study Next

10. Common Errors / Trap Areas

11. Sources

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