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PMAY-G Survey to use ‘Face Recognition’ tools to identify new Beneficiaries
Context:
A new survey for the rural housing (PMAY-G) scheme aims to identify 8 million additional beneficiaries, alongside the existing 12 million, using advanced face-recognition tools to minimise gaps.
About PMAY-G:
- Launch of Flagship Scheme: The Pradhan Mantri Awaas Yojana – Gramin (PMAY-G) is a flagship rural development program launched by Prime Minister Narendra Modi on November 20, 2016.
- Vision: The scheme aims to fulfil the government’s vision of “Housing for All” by providing affordable and quality housing to the rural poor.
- Objectives: PMAY-G ensures inclusivity by prioritising women beneficiaries, the economically weaker sections, and marginalised communities, with ownership primarily in the name of women or as joint ownership.
Achievements in the First Phase (2016-24):
- Sanctioned and Completed Houses: Out of the 29.5 million houses sanctioned, 27 million have already been constructed.
- Saturation in 20 States: Based on the Socio-Economic Caste Census (SECC) 2011 and the first Awasplus survey conducted in 2018, 20 out of 34 states have achieved saturation in beneficiary coverage.
- Empowerment of Women: Approximately 74% of the houses were allotted to women beneficiaries or as joint ownership during the initial phase. By 2024, 100% of houses are being sanctioned under the same criteria, ensuring gender inclusivity and financial empowerment.
Post-2024 Targets Under PMAY-G:
The government has set ambitious goals for the next five years.
- 20 Million New Rural Houses: The scheme plans to construct an additional 20 million houses by 2029.
- Immediate Focus: The identification of at least 8 million new beneficiaries in addition to the existing 12 million identified.
- Annual Targets: For 2024-25, the goal is to construct 3.8 million houses, reflecting a structured and systematic approach to scaling the program.
Methods of Identifying Beneficiaries:
The identification process for PMAY-G has evolved over the years to ensure accuracy and inclusivity.
- SECC 2011: Initially, beneficiary identification was based on data from the SECC 2011, providing a foundational list.
- First Awasplus Survey (2018): Conducted to address gaps in SECC 2011 data, identifying additional eligible households overlooked in the original survey.
- Second Awasplus Survey (2024): Aimed at identifying 20 million rural houses over five years, this survey focuses on including first-time beneficiaries and addressing regional gaps.
New Face Recognition Survey Post-2024:
To enhance accuracy and transparency, PMAY-G is incorporating advanced technology for beneficiary identification.
- Face Recognition Tools: These tools ensure accurate identification, reduce duplication, and eliminate errors in the beneficiary database.
- Self-Survey via Awaassoft App: Beneficiaries can conduct their own survey using the mobile application, which is allowed only once per device to maintain data integrity.
Benefits of the New Survey Approach:
- Accuracy: Face recognition ensures that beneficiaries are correctly identified, minimising errors and fraudulent claims.
Need for the New Survey Type:
Despite the success of traditional methods, gaps in coverage and inclusion persisted, necessitating a more robust mechanism.
- Challenges in Manual Surveys: Errors in data collection, duplication, and exclusion of eligible beneficiaries highlighted the limitations of older methods.
- Role of Technology: Face recognition and self-surveys ensure that every eligible individual is accounted for, aligning with the government’s objective of equitable and transparent resource allocation.
- Transparency: The self-survey process empowers individuals while ensuring data integrity.
- Efficiency: Automation of the survey process speeds up data collection and analysis, reducing manual effort and delays.
Comparison with Existing Surveys::
- Traditional Surveys (SECC and Awasplus): Relied on manual enumeration and verification, prone to errors and delays.
- Face Recognition-Based Surveys: Incorporate advanced technology, enhancing accuracy and significantly reducing scope for duplication or exclusion.
