Policy-Driven Deep Learning Framework for Facial Age Verification in Senior Citizen Welfare Programs

One of the main requirements is the eligibility on the basis of age. In most of the elder citizen welfare programs.However, traditional document-based checking systems are still susceptible to manipulation and falsification of claims. This study proposes a deep deterrence age verification paradigm to aid in the equitable distribution of elderly benefits, which is resistant to
fraud. Instead of pre-emptive age discrimination, the problem is subjected to a policy-reformulation. Discrimination is an aligned
binary classification task that differentiates individuals. under 60 years compared to 60 years and above. A large-scale database with over 160,000 images obtained. Two open-source repositories are used in model training. and evaluation.Three convolutional neural network architectures. Comparatively three architectuere,
VGG16, EfficientNet-B3, and ResNet-50, are comparable. that were processed under the same experimental conditions. To address lopsidedness and minimize the chances of abandoning
deserving elderly. persons, a class-conscious training practice of selective. Weighted loss optimization and data augmentation are used. The proposed framework has proven successful after
experimental results. obtains good F1-scores and high accuracy, and at the same time. strong recall among the older age group of 60 +. The findings indicate that the suggested solution offers a policy-congruent, trustworthy, and age verification Automated solution that is a socially responsible one. real-world welfare
systems.