Objective

To create a system that automatically detects and verifies the gender and age of individuals for voter card applications, ensuring accuracy and reducing errors in the electoral database.

Key Components

1. Input Data
   • High-quality scanned images of voter card applications or user-provided photographs.
   • Basic demographic details (e.g., name, DOB).
2. Technologies Used
   • Face Recognition Libraries: OpenCV, Dlib, or DeepFace for analyzing facial features.
   • Machine Learning Models: Pre-trained models for gender and age detection (e.g., TensorFlow, PyTorch).
   • OCR Tools: Tesseract or AWS Textract to extract text (e.g., date of birth) from images.
3. Workflow
   • Step 1: Input the voter’s photo and DOB (optional).
   • Step 2: Use OCR to extract the DOB from documents if not provided directly.
   • Step 3: Process the photo with a gender and age detection model.
   • Step 4: Compare detected age with the extracted DOB for validation.
   • Step 5: Flag inconsistencies (e.g., if the detected age significantly differs from the DOB).
4. Architecture
   • Frontend: A simple web or mobile app for uploading images and entering details.
   • Backend:
     • Image processing with OpenCV or similar tools.
     • Model integration for age and gender detection.
   • Database: To store user details and results for future reference.

Features

1. Age Detection
   • Use convolutional neural networks (CNNs) trained on datasets like IMDB-WIKI or UTKFace.
   • Predict age based on facial features with a certain margin of error (e.g., ±3 years).
2. Gender Detection
   • Classify gender based on facial structure using pre-trained models.
3. Verification Against Documents
   • Cross-verify extracted DOB with detected age.
   • Identify discrepancies, such as mismatched DOB or altered images.
4. Error Handling
   • Manual override for cases where automated detection is inconclusive.

Challenges

1. Accuracy of Models: Variations in lighting, angle, and image quality can affect results.
2. Data Privacy: Ensure compliance with data protection laws (e.g., GDPR, CCPA).
3. Edge Cases