Product Requirements Document (PRD)

Face Condition Detection in Any Lighting

Document Version: 1.0
Date: March 14, 2026
Status: Active
Organization: CCExtractor Organization (GSOC 2026)
Project Type: Mobile Application

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1. Executive Summary

Product Vision

Face Condition Detection in Any Lighting is an intelligent mobile application that provides real-time analysis of facial detection quality across varying lighting environments. The product combines advanced computer vision techniques with intuitive user feedback mechanisms to help users optimize face positioning, lighting conditions, and detection parameters—enabling reliable face detection in any scenario.

Product Statement

"Enable accurate, real-time facial detection and quality assessment across any lighting condition through an intelligent, user-friendly mobile application that adapts to environmental factors and provides actionable guidance."

Primary Use Cases

1. Face Recognition Pipeline Quality Assurance: Pre-verification of face quality before submission to recognition systems
2. Video Conferencing Optimization: Automatic detection of poor video quality parameters
3. Biometric Authentication: Optimization of facial biometric capture conditions
4. Accessibility Applications: Guidance for visually-impaired users to position faces correctly
5. Media Capture Assistants: Help users take quality photos/videos with proper face detection
6. Security/Surveillance Systems: Real-time feedback on camera positioning and lighting

Target Metrics

• Detection Accuracy: > 95% in optimal conditions, > 85% in challenging conditions
• Response Latency: < 100ms per frame analysis
• User Satisfaction: > 4.0/5.0 rating
• Crash Rate: < 0.1%
• Installation Size: < 150 MB

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2. Problem Statement

Business Problem

Current face detection applications lack:

• Insufficient Environmental Adaptation: No intelligent response to varying lighting
• Poor User Guidance: Minimal feedback on how to improve detection quality
• Limited Diagnostics: Absence of real-time metrics on brightness, contrast, and face position
• Inconsistent Results: Detection success highly variable based on conditions
• User Frustration: Users unclear why faces aren't detected or quality is poor

Target Users' Pain Points

1. Users attempting face registration: Unclear what improves detection success
2. Developers integrating face APIs: No environment assessment before API calls
3. Customer service teams: No tool to help customers troubleshoot detection issues
4. Security/authentication systems: Inconsistent capture quality reduces user experience
5. Content creators: No guidance for optimal face visibility in streams/videos

Market Opportunity

• Growing demand for accessible face detection tools
• Increased adoption of biometric security systems
• Rise of video content creation requiring quality assurance
• Need for: offline, privacy-preserving face quality tools
• Estimated market: Millions of potential users across authentication, compliance, and content applications

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3. Product Goals and Objectives

Strategic Goals

Goal 1: Enable Reliable Face Detection in Any Condition

Objective: Provide users with tools to achieve optimal face detection regardless of environmental factors

• All users receive quality guidance
• Lighting assessment accuracy > 90%
• Detection success > 95% when recommendations followed

Goal 2: Privacy-First Face Detection

Objective: Ensure all processing occurs locally without data transmission

• Zero external API calls for personal data
• No image storage or transmission
• Full offline functionality

Goal 3: Intuitive User Experience

Objective: Enable users of all technical levels to optimize face detection

• Color-coded feedback indicators
• Clear, actionable recommendations
• < 30 second learning curve

Goal 4: Cross-Platform Reliability

Objective: Ensure consistent experience across iOS and Android

• Identical UI/UX across platforms
• Equivalent detection performance
• Unified feature set

Goal 5: Developer-Friendly Architecture

Objective: Enable easy integration and customization

• Modular component design
• Clear API interfaces
• Comprehensive documentation

Key Performance Indicators (KPIs)

KPI	Target	Measurement
Detection Accuracy	> 95% optimal, > 85% challenging	Frame analysis success rate
Response Time	< 50ms per frame	Frame-to-result latency
User Retention	> 70% after 24 hours	App re-launch rate
Crash Rate	< 0.1%	Crashes per 1,000 sessions
User Satisfaction	> 4.0/5.0	App store rating
Installation Success	> 99%	Install completion rate
Frame Processing	> 30 FPS	Frames processed per second

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4. Requirements Overview

Functional Requirements

FR-1: Face Detection

• Requirement: Real-time detection of human faces in camera stream
• Details:
  • Detect single and multiple faces simultaneously
  • Process minimum 30 frames per second
  • Utilize Google ML Kit for detection
  • Support both portrait and landscape orientations
• Success Criteria:
  • Detection latency < 100ms
  • Accuracy > 95% in optimal conditions

FR-2: Lighting Condition Analysis

• Requirement: Categorize environmental lighting into 5 distinct conditions
• Categories:
  1. Too Dark (Brightness < 10%)
  2. Dark (Brightness 10-30%)
  3. Optimal (Brightness 30-70%)
  4. Bright (Brightness 70-85%)
  5. Too Bright (Brightness > 85%)
• Details:
  • Calculate brightness using standard luminance formula
  • Update lighting assessment in real-time
  • Provide visual indicators for each condition
• Success Criteria:
  • Lighting categorization accuracy > 90%
  • Assessment latency < 50ms

FR-3: Quality Assessment

• Requirement: Evaluate detected faces on 5-level quality scale
• Quality Levels:
  1. Not Detected - No face in frame
  2. Poor - Multiple faces or extreme lighting
  3. Fair - Suboptimal lighting or multiple faces
  4. Good - Single face in good lighting
  5. Excellent - Optimal conditions with high contrast
• Details:
  • Consider face count, lighting, and contrast in assessment
  • Update quality in real-time
  • Provide confidence scores
• Success Criteria:
  • Quality assessment accuracy matches user perception > 85%

FR-4: Real-Time Metrics Display

• Requirement: Display key metrics to inform user decisions
• Metrics:
  • Brightness percentage (0-100%)
  • Contrast level (0-100%)
  • Exposure adjustment recommendation (-1.0 to +1.0)
  • Face count
  • Detection confidence score
• Details:
  • Update metrics every frame (30ms cadence)
  • Display metrics overlay on camera feed
  • Ensure metrics don't obscure face detection
• Success Criteria:
  • All metrics visible and readable
  • Updates occur with no perceptible lag

FR-5: Contextual Recommendations

• Requirement: Provide actionable guidance based on current conditions
• Recommendation Types:
  • Lighting adjustment ("Move to brighter area")
  • Distance guidance ("Move closer/farther")
  • Face positioning ("Face camera directly")
  • Multiple face alerts ("Only one face should be in frame")
  • Specific improvement suggestions
• Details:
  • Generate recommendations automatically based on detected conditions
  • Update recommendations in real-time
  • Prioritize most critical improvements
• Success Criteria:
  • Recommendations are actionable and understood by > 90% of users
  • Following recommendations improves detection by > 40%

FR-6: Color-Coded Status Indicators

• Requirement: Provide visual feedback through color coding
• Mapping:
  • Red: Poor/needs improvement
  • Orange: Suboptimal
  • Yellow: Acceptable
  • Green: Good/optimal
  • Dark Green: Excellent
• Details:
  • Apply color coding to quality and lighting indicators
  • Use consistent color scheme throughout app
  • Ensure accessibility for color-blind users (include patterns/text)
• Success Criteria:
  • Color coding correctly indicates status > 99% of time
  • Accessible to users with color blindness

FR-7: Camera Stream Handling

• Requirement: Efficiently capture and process camera frames
• Details:
  • Access device front-facing camera
  • Process frames at minimum 30 FPS
  • Handle portrait and landscape orientations
  • Support multiple camera formats (NV21, BGRA8888)
  • Gracefully handle camera access denial
• Success Criteria:
  • Camera stream stable for > 5 minutes
  • Frame processing maintains > 25 FPS on mid-range devices

FR-8: Permission Management

• Requirement: Request and manage camera permissions appropriately
• Details:
  • Request camera permission on first launch
  • Handle permission denial gracefully
  • Provide user guidance for permission re-grant
  • Re-request permission if previously denied
• Success Criteria:
  • Permission flows work on 100% of tested devices
  • Users can override permission denial

FR-9: Cross-Platform Consistency

• Requirement: Maintain equivalent functionality across iOS and Android
• Details:
  • Identical UI/UX on both platforms
  • Same detection algorithm on both platforms
  • Equivalent performance targets met on both
  • Common code base where possible using Flutter
• Success Criteria:
  • Feature parity on Android and iOS
  • Performance within 10% between platforms

FR-10: Responsive Design

• Requirement: Support multiple screen sizes and orientations
• Details:
  • Support phone and tablet form factors
  • Adapt layout for portrait and landscape
  • Ensure readability on 4" to 12"+ screens
  • Maintain usability in both orientations
• Success Criteria:
  • UI renders correctly on all target devices
  • No horizontal or vertical scrolling needed

Non-Functional Requirements

NFR-1: Performance

• Requirement: Maintain real-time responsiveness
• Targets:
  • Frame processing: < 50ms per frame
  • UI update: 30+ FPS
  • Detection latency: < 100ms
  • Memory footprint: < 200 MB during operation
  • App startup: < 3 seconds
• Details:
  • Optimize ML Kit inference
  • Implement efficient image processing
  • Use asynchronous processing where possible
  • Monitor and optimize memory usage
• Success Criteria:
  • 95% of frames processed within latency targets
  • Sustained performance for > 10 minutes

NFR-2: Reliability

• Requirement: Ensure app stability and error handling
• Targets:
  • Crash rate: < 0.1% of sessions
  • Error recovery: Graceful for all error scenarios
  • Data integrity: No lost analysis results due to errors
• Details:
  • Implement comprehensive error handling
  • Graceful degradation on error
  • Automatic recovery mechanisms
  • Proper logging for debugging
• Success Criteria:
  • No unhandled exceptions
  • App recovers from all tested error conditions

NFR-3: Security and Privacy

• Requirement: Protect user privacy and data security
• Targets:
  • Zero external data transmission
  • No persistent storage of faces/images
  • Offline-first architecture
• Details:
  • All processing local to device
  • No cloud connectivity required
  • No analytics tracking
  • Minimal required permissions
• Success Criteria:
  • No network calls for personal data
  • No face data in device storage
  • Users understand privacy model

NFR-4: Accessibility

• Requirement: Ensure usability for users with disabilities
• Details:
  • Support for accessible text sizing
  • High-contrast mode option
  • Color-blind friendly indicators
  • Screen reader compatible UI elements
  • Haptic feedback possible
• Success Criteria:
  • WCAG 2.1 Level AA compliance
  • Accessibility audit passes

NFR-5: Compatibility

• Requirement: Support wide range of devices
• Targets:
  • Android: 5.0+ (API 21+), up to latest
  • iOS: 12.0+, up to latest
  • Device RAM: Works on 2GB minimum
  • Device storage: < 150MB installation size
• Details:
  • Test on range of device models
  • Optimize for low-end and high-end devices
  • Handle device capability variations
• Success Criteria:
  • Installs and runs on 99% of target devices
  • Installation size maintained

NFR-6: Usability

• Requirement: Ensure intuitive user experience
• Targets:
  • Learning curve: < 30 seconds
  • Success rate for new users: > 90% on first use
  • User satisfaction: > 4.0/5.0 rating
• Details:
  • Intuitive interface design
  • Minimal text, maximum visual feedback
  • Clear, actionable recommendations
  • Contextual help
• Success Criteria:
  • User testing confirms intuitive design
  • Support tickets < 5% of user base

NFR-7: Maintainability

• Requirement: Enable ongoing development and maintenance
• Details:
  • Modular architecture
  • Clear separation of concerns
  • Comprehensive code documentation
  • Unit and integration test coverage
  • Version control best practices
• Success Criteria:
  • New developers can contribute within 1 week
  • Code review process established
  • Documentation maintained

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5. User Stories and Scenarios

User Story 1: First-Time User Setup

As a new user installing the app for the first time
I want to quickly understand how to use the app and start detecting faces
So that I can immediately begin optimizing my face detection quality

Acceptance Criteria:

• App launches successfully within 3 seconds
• Camera permission dialog appears on first launch
• After granting permission, live camera feed is visible
• User can see real-time face detection results
• Learning time < 30 seconds

User Story 2: Suboptimal Lighting Detection

As a user in a poorly-lit environment
I want to understand that my lighting is suboptimal and receive suggestions
So that I can adjust my environment for better face detection

Acceptance Criteria:

• Lighting condition "Dark" or "Too Dark" is correctly detected
• Visual indicator changes color (red/orange)
• Recommendation suggests "Move to brighter area" or similar
• User gains understanding that lighting needs improvement

User Story 3: Quality Verification

As a user needing to verify face quality before submitting to a system
I want to see a clear quality assessment and whether it's acceptable
So that I can decide whether to resubmit or adjust conditions

Acceptance Criteria:

• Quality level is displayed (Not Detected/Poor/Fair/Good/Excellent)
• Visual indicator clearly shows quality status
• Color coding indicates acceptability
• User can confidently decide to proceed or adjust

User Story 4: Multiple Faces Alert

As a user with someone in the background of the frame
I want to know that multiple faces are detected and affecting quality
So that I can reposition to have only one face in frame

Acceptance Criteria:

• App detects multiple faces
• Quality assessment drops (Fair or Poor)
• Recommendation alerts user to "Ensure only one face in frame"
• User understands they should reposition

User Story 5: Optimal Conditions

As a user with optimal lighting and proper positioning
I want to see confirmation that conditions are ideal
So that I can confidently proceed with face-dependent actions

Acceptance Criteria:

• Lighting condition shows "Optimal"
• Quality level shows "Good" or "Excellent"
• Visual indicators show green colors
• Recommendation confirms "Perfect conditions!"

User Story 6: Real-Time Guidance

As a user adjusting my position
I want to see metrics update in real-time as I move
So that I can quickly find optimal positioning

Acceptance Criteria:

• Brightness percentage updates continuously
• Quality level changes as I reposition
• Recommendation updates based on new conditions
• Updates occur smoothly with no noticeable lag

User Story 7: Permission Management

As a user who initially denied camera permission
I want to be able to re-grant permission and use the app
So that I don't need to reinstall the app

Acceptance Criteria:

• App detects permission denial
• User-friendly message explains why camera is needed
• Message includes guidance to go to settings
• User can successfully re-grant permission
• App functions normally after permission re-grant

User Story 8: Different Lighting Conditions

As a a user in different environments (home, office, outdoors, low-light)
I want to see how the app adapts to each environment
So that I can optimize face detection in any location

Acceptance Criteria:

• App correctly categorizes lighting in each environment
• Brightness percentage matches perceived lighting
• Recommendations are appropriate for each environment
• App provides useful guidance in all scenarios

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6. Feature Requirements Details

Feature Set Breakdown

Feature 1: Real-Time Face Detection Engine

Description: Core face detection using ML Kit
Business Value: Enables primary product function
Complexity: HIGH
Priority: P0 (Must-Have)
Dependencies:

• Google ML Kit library
• Camera access
• Device hardware with camera

Acceptance Criteria:

• Detects faces at 30+ FPS
• Detects multiple faces simultaneously
• Provides confidence scores
• Handles various face angles/rotations
• Works on both Android and iOS

Feature 2: Lighting Analysis Engine

Description: Calculate brightness and categorize lighting
Business Value: Enables environmental adaptation
Complexity: MEDIUM
Priority: P0 (Must-Have)
Dependencies:

• Image processing library
• Brightness calculation algorithms
• Real-time frame analysis

Acceptance Criteria:

• Brightness calculated from every frame
• Accuracy > 90% vs. reference light meter
• Classifications match manual assessment
• Latency < 50ms per frame

Feature 3: Quality Assessment Algorithm

Description: Determine face quality on 5-level scale
Business Value: Key decision-making tool for users
Complexity: MEDIUM
Priority: P0 (Must-Have)
Dependencies:

• Face detection results
• Lighting analysis
• Contrast calculation

Acceptance Criteria:

• All 5 quality levels achievable
• Quality assessment matches user expectations
• Correctly identifies when quality is acceptable

Feature 4: Recommendation Engine

Description: Generate contextual, actionable recommendations
Business Value: Enables users to self-improve conditions
Complexity: MEDIUM
Priority: P0 (Must-Have)
Dependencies:

• Quality assessment
• Lighting analysis
• Face detection data

Acceptance Criteria:

• Recommendations are actionable
• Following recommendations improves detection
• Multiple suggestions when multiple issues exist
• Recommendations understood by > 90% of users

Feature 5: User Interface

Description: Camera feed with overlay metrics and status
Business Value: Primary interaction point for users
Complexity: MEDIUM
Priority: P0 (Must-Have)
Dependencies:

• Flutter framework
• Material Design
• Real-time analysis results

Acceptance Criteria:

• Camera feed visible without lag
• All metrics readable
• Color coding consistent and clear
• Responsive to screen size changes

Feature 6: Permission Management

Description: Request and handle camera permissions
Business Value: Enables app functionality, maintains user trust
Complexity: LOW
Priority: P0 (Must-Have)
Dependencies:

• permission_handler library
• Native Android/iOS APIs

Acceptance Criteria:

• Permission requested on first launch
• Handles denial gracefully
• Allows permission re-grant
• Works on Android 5.0+ and iOS 12.0+

Feature 7: Contrast Analysis

Description: Calculate image contrast for quality assessment
Business Value: Enables accurate quality determination
Complexity: LOW
Priority: P1 (Should-Have)
Dependencies:

• Image processing
• Statistical calculations

Acceptance Criteria:

• Contrast calculated accurately
• Correlates with image quality
• Normalized to 0-100% range

Feature 8: Exposure Adjustment Recommendation

Description: Suggest exposure adjustments (-1.0 to +1.0)
Business Value: Technical guidance for power users
Complexity: LOW
Priority: P2 (Nice-to-Have)
Dependencies:

• Brightness analysis
• Camera exposure APIs (if applicable)

Acceptance Criteria:

• Adjustment value correctly represents needed change
• Range -1.0 to +1.0 adequately represents adjustment spectrum

Feature 9: Orientation Support

Description: Support portrait and landscape orientations
Business Value: Enables use in any orientation
Complexity: LOW
Priority: P1 (Should-Have)
Dependencies:

• Flutter orientation handling
• Responsive layout design

Acceptance Criteria:

• App works in both orientations
• UI adapts appropriately
• Camera feed continues processing

Feature 10: Offline Functionality

Description: Complete offline operation without cloud dependencies
Business Value: Privacy and reliability
Complexity: MEDIUM
Priority: P0 (Must-Have)
Dependencies:

• ML Kit library
• Local processing only

Acceptance Criteria:

• No cloud API calls required
• No external dependencies for core functionality
• Works without internet connection

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7. User Experience Requirements

UI/UX Principles

1. Simplicity First: Minimize cognitive load with intuitive design
2. Real-Time Feedback: Immediate visual response to user actions
3. Color-Coded Communication: Consistent use of colors for status
4. Accessibility: Support for users with disabilities
5. Consistency: Uniform design and behavior across screens
6. Responsiveness: Adapt to device sizes and orientations
7. Progressive Complexity: Simple baseline with advanced options available

Visual Design

Color Scheme

• Primary Colors:
  • Deep Purple (#673AB7): Brand primary
  • Light Green (#4CAF50): Success/Good status
  • Orange (#FF9800): Warning/Suboptimal
  • Red (#F44336): Error/Poor status
  • Dark Gray (#424242): Neutral/Background

Typography

• Font Family: System default (San Francisco on iOS, Roboto on Android)
• Font Sizes:
  • Heading: 20-24pt
  • Body: 14-16pt
  • Small text: 12pt
  • Metrics: 18-20pt

Layout

• Safe Margins: 16dp on sides, top, bottom
• Component Spacing: 8-16dp between elements
• Overlay Opacity: 70-80% for readability over camera feed

User Flow

Launch App
    ↓
Request Permission
    ├─ Granted → Initialize Camera
    └─ Denied → Show Permission Dialog
         ↓
      User Goes to Settings
         ↓
      Re-Grant Permission → Initialize Camera
    ↓
Load ML Kit Model
    ↓
Start Camera Stream
    ↓
Display Live Camera Feed
    ├─ Face Detected
    │  ├─ Single Face → Quality Assessment
    │  │  ├─ Display Quality Level
    │  │  ├─ Display Metrics (Brightness, Contrast, etc.)
    │  │  └─ Display Recommendation
    │  │
    │  └─ Multiple Faces → Alert User
    │
    └─ No Face → Show "No Face Detected"
         ↓
      Continuous Real-Time Updates

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8. Product Scope

In Scope (Must-Have)

• Real-time face detection for single and multiple faces
• Lighting condition analysis (5 categories)
• Face quality assessment (5 levels)
• Real-time metrics display (brightness, contrast, exposure)
• Contextual recommendations
• Color-coded status indicators
• Camera permission handling
• Portrait and landscape support
• iOS and Android support
• Offline functionality

Out of Scope (Future Enhancements)

• Cloud-based face recognition
• Face emotion detection
• Face landmark detection
• Image/video recording
• Face match against database
• Batch processing
• Cloud storage integration
• Third-party API integrations
• Advanced ML models (Emotion, Age, Gender)
• Augmented Reality overlays
• Multi-language support (initially English only)

Deferred (Version 2.0+)

• Video recording with detection overlays
• Face templates/snapshots
• Historical analysis data
• Advanced statistics dashboard
• Integration with external services
• Custom model support
• Real-time face recognition

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9. Success Metrics and KPIs

Adoption Metrics

• Installation Rate: Target 10K+ installs in first month
• Daily Active Users (DAU): Target 2K+ DAU after stabilization
• Monthly Active Users (MAU): Target 5K+ MAU
• User Retention Day 1: > 70% of installers launch next day
• User Retention Day 7: > 50% continue using after one week

Quality Metrics

• Detection Accuracy: > 95% in optimal conditions
• False Positive Rate: < 5% (no false face detections)
• False Negative Rate: < 5% (missing real faces)
• Crash Rate: < 0.1% per session
• App Store Rating: Target 4.0+ stars

Performance Metrics

• App Startup Time: < 3 seconds
• Frame Processing Latency: < 50ms
• UI Responsiveness: 30+ FPS sustained
• Memory Usage: < 200MB during operation
• Battery Consumption: < 5% per hour of use

User Engagement Metrics

• Session Duration: Average > 2 minutes
• Feature Usage: > 90% use quality assessment
• Recommendation Following: > 60% act on recommendations
• User Satisfaction: > 4.0/5.0 rating

Business Metrics

• Cost per User: Target < $0.50
• Lifetime Value: > $2.00 (if monetized)
• Support Ticket Rate: < 2% of user base
• NPS Score: Target 50+

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10. Release Plan and Timeline

Phase 1: MVP (Minimum Viable Product) - v1.0

Timeline: Initial Release (March 2026) Features:

• Real-time face detection
• Lighting condition analysis (5 categories)
• Quality assessment (5 levels)
• Basic metrics display
• Recommendation engine
• Permission handling
• Android and iOS support

Deliverables:

• Production app build
• README documentation
• Basic testing completed

Phase 2: Refinement and Stability - v1.1

Timeline: Q2 2026 (3 months post-launch) Features:

• Performance optimization
• Bug fixes from user feedback
• Enhanced recommendations
• Better error handling
• Improved accessibility
• Documentation updates

Targets:

• Crash rate < 0.1%
• Performance > 95% within SLA
• User satisfaction > 4.0 stars

Phase 3: Advanced Features - v2.0

Timeline: Q3-Q4 2026 (6-12 months post-launch) Features:

• Video recording with overlays
• Face landmarks visualization
• Historical data tracking
• Statistics dashboard
• Advanced recommendation algorithm
• Multi-language support
• Customization options

Targets:

• New user acquisition
• Increased session duration
• Improved retention

Phase 4: Integration and Expansion - v3.0

Timeline: 2027 Features:

• Third-party API integrations
• Cloud storage options
• Face recognition capabilities
• Batch processing
• Enterprise features

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11. Assumptions and Dependencies

Assumptions

1. Users have front-facing camera on their device
2. Target users are comfortable with mobile apps
3. Market demand exists for face quality tools
4. ML Kit will remain available and functional
5. Flutter will continue as primary framework
6. Online/offline operation is feasible on target devices
7. Users will accept privacy-first, offline-only approach

Dependencies

1. Google ML Kit: Face detection capabilities
2. Flutter Framework: Cross-platform development
3. Camera Plugin: Device camera access
4. Image Processing Library: Brightness/contrast calculation
5. Permission Handler: OS-level permission management
6. Device Hardware: Camera hardware on target devices
7. Android/iOS SDKs: Platform-specific development

External Constraints

1. Privacy Regulations: GDPR, CCPA compliance
2. Platform Policies: Apple App Store, Google Play Store guidelines
3. Device Fragmentation: Wide variety of Android devices
4. Network: Offline-first design eliminates dependency
5. Power Consumption: Optimization needed for battery life

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12. Risks and Mitigation

High Severity Risks

Risk: Poor Detection Accuracy in Low Light

Impact: Product fails in key use case
Probability: Medium
Mitigation:

• Extensive testing in various lighting conditions
• Dynamic threshold adjustment
• Clear guidance to users on optimal conditions
• Fallback recommendations for poor conditions

Risk: High Battery Consumption

Impact: Users uninstall due to battery drain
Probability: Medium
Mitigation:

• Optimize frame processing
• Implement adaptive processing rates
• Test on low-end devices
• Provide battery-saving mode option

Risk: Crash on Certain Devices

Impact: Poor reviews, uninstalls
Probability: Medium
Mitigation:

• Extensive device testing
• Crash logging and monitoring
• Regular error analysis and fixes
• Wide device compatibility matrix

Medium Severity Risks

Risk: User Confusion Understanding Recommendations

Impact: Poor user satisfaction
Probability: Medium
Mitigation:

• Extensive user testing of UI
• A/B testing of recommendations
• In-app help and tutorials
• Community feedback channels

Risk: ML Kit Model Updates Break App

Impact: Unexpected behavior changes
Probability: Low
Mitigation:

• Version pinning of ML Kit
• Testing for new versions before adoption
• Gradual rollout of new versions
• Fallback mechanisms

Risk: High Memory Usage on Low-End Devices

Impact: Performance issues or crashes
Probability: Medium
Mitigation:

• Memory optimization
• Low-memory mode option
• Device capability detection
• Streaming vs. buffering optimization

Low Severity Risks

Risk: Frame Rate Inconsistency

Impact: Suboptimal user experience
Probability: Low
Mitigation:

• Frame rate optimization
• Adaptive quality adjustment
• Performance profiling
• Hardware acceleration usage

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13. Success Criteria

Must-Have (Must Pass)

• ✅ Real-time face detection working at 30+ FPS
• ✅ Lighting categorization > 90% accurate
• ✅ Quality assessment matches user expectations
• ✅ App crash rate < 0.1%
• ✅ Permissions flow works on 100% of tested devices
• ✅ Identical feature set on iOS and Android

Should-Have (Should Pass)

• ✅ User satisfaction > 4.0/5.0 rating
• ✅ Installation successful on 99% of target devices
• ✅ Performance targets met on mid-range devices
• ✅ Accessibility WCAG 2.1 AA compliance
• ✅ Support ticket rate < 5% of user base

Nice-to-Have (If Time Permits)

• ✅ Advanced statistics dashboard
• ✅ Video recording capabilities
• ✅ Multiple language support
• ✅ Face landmarks overlay
• ✅ Batch processing mode

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14. Sign-Off and Approval

This PRD is approved for implementation and defines the product vision and requirements for Face Condition Detection in Any Lighting v1.0.

Role	Name	Signature	Date
Product Manager	-	-	March 14, 2026
Technical Lead	-	-	March 14, 2026
Project Manager	-	-	March 14, 2026

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Appendix A: Technical Architecture Overview

High-Level Architecture

Camera Feed
    ↓
Camera Stream Handler
    ↓
Image Converter
    ↓
Face Detection Engine (ML Kit)
    ↓
Face Condition Analyzer
├─ Brightness Calculator
├─ Contrast Calculator
├─ Lighting Categorizer
├─ Quality Assessor
└─ Recommendation Generator
    ↓
UI Rendering Engine
├─ Real-Time Metrics Display
├─ Status Indicators
└─ Recommendation Display

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Appendix B: Glossary

• ML Kit: Google's Machine Learning Kit for mobile development
• FPS: Frames Per Second
• Latency: Time delay in processing
• Brightness: Perceived lightness of an image (0-100%)
• Contrast: Difference in luminance between light and dark areas
• Confidence Score: Model's certainty in detection (0-100%)
• Face Quality: Assessment of face suitability for detection/recognition
• Lighting Condition: Category of environmental lighting
• DAU: Daily Active Users
• MAU: Monthly Active Users
• KPI: Key Performance Indicator
• MVP: Minimum Viable Product
• PRD: Product Requirements Document
• UI/UX: User Interface / User Experience

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Document ID: PRD-FCDv1.0
Last Updated: March 14, 2026
Status: Active / Published