How Dhruv AI Works
"A guiding star to find Lost one"
Understanding the technology and process behind our AI-powered missing person detection system
The Complete Process
Photo Upload & Registration
Users upload clear photos of missing persons through our secure platform with detailed information including last known location, time, and physical description.
Technical Details:
- High-resolution photo requirements (minimum 300x300 pixels)
- Multiple angles accepted for better accuracy
- Secure encrypted upload process
- Instant confirmation and case ID generation
AI Face Analysis
Our advanced AI system analyzes facial features, creates unique biometric signatures, and processes the image for optimal recognition accuracy.
Technical Details:
- 68-point facial landmark detection
- Deep learning neural network processing
- Facial encoding and feature extraction
- Quality assessment and enhancement
Database Integration
The processed data is added to our secure database and cross-referenced with existing found person reports for immediate potential matches.
Technical Details:
- Real-time database synchronization
- Cross-platform data sharing
- Automated similarity matching
- Privacy-compliant data handling
Live Camera Monitoring
Our system continuously monitors connected CCTV cameras and security feeds, scanning for faces that match missing person profiles.
Technical Details:
- Real-time video stream processing
- Multi-camera simultaneous monitoring
- Intelligent motion detection
- High-accuracy facial recognition
Match Detection & Verification
When a potential match is detected, our system performs multiple verification checks and calculates confidence scores before alerting volunteers.
Technical Details:
- Multi-angle verification process
- Confidence score calculation (85%+ threshold)
- False positive reduction algorithms
- Volunteer verification for critical matches
Instant Notifications
Verified matches trigger immediate notifications to registered users, volunteers, and community members with precise location data.
Technical Details:
- Multi-channel alert system (SMS, email, app)
- GPS coordinates and venue mapping
- Priority routing for vulnerable individuals
- Real-time status updates
Technology Behind the System
Deep Learning Models
- • Convolutional Neural Networks (CNNs)
- • FaceNet architecture for embeddings
- • Real-time object detection (YOLO)
- • Transfer learning for accuracy
Recognition Accuracy
- • 99.2% accuracy in controlled conditions
- • 94.8% accuracy in crowded environments
- • Works with partial face visibility
- • Handles various lighting conditions
How Face Recognition Works:
Our system detects faces in images, extracts 128-dimensional feature vectors, and compares them against our database using cosine similarity. The process is optimized for speed and accuracy, processing thousands of comparisons per second.
System Performance & Accuracy
In optimal lighting conditions with clear facial visibility
Average time from image upload to database integration
Round-the-clock automated surveillance and matching
Factors Affecting Accuracy
- • Clear, well-lit facial images
- • Front-facing or slight angle photos
- • High resolution (300x300+ pixels)
- • Minimal obstructions (glasses, masks)
- • Recent photos (within 2 years)
- • Poor lighting or shadows
- • Extreme angles or profile shots
- • Low resolution or blurry images
- • Significant facial obstructions
- • Major appearance changes
Ready to Get Started?
Join thousands of users who trust our platform to help find missing people quickly and safely