VisionAIre
  • About Nodeflux
  • Visionaire Stream
    • Overview
    • Requirements
      • Credential Keys
      • Tested Hardware
      • Bandwith Requirements
    • Installation Guide
      • Dependencies
      • Simple Installation
      • Advanced Installation
      • Clustering Installation
      • Add-ons Analytics Installation
        • People Attributes Installation
        • Vehicle Attributes Installation
        • OVOD Installation
        • OVIC Installation
        • VM Installation
        • VLM Installation
    • Analytics
      • Face Recognition
        • Additional Information
          • Overview
          • Disclaimer
          • Metrics
          • Testing
      • People Analytics
      • Crowd Estimation
      • PPE Detection
      • License Plate Recognition
      • Vehicle Analytics
      • Water Level Monitoring
        • Camera Guideline
      • Pre-Flood Monitoring
      • Person Smoking Detection
      • Person with Handphone Detection
      • Smoke and Fire Detection
      • Person Using Firearms Detection
      • Vandalism Attempt Recognition
      • ATM Burglary Incident Recognition
      • Road Crash Monitoring
      • People Fighting Recognition
      • Riot Recognition
    • Developer Guide
      • How our APIs work
      • HTTP APIs
      • Websocket
      • Database Structure
    • Changelogs
  • Visionaire Snapshot
    • Overview
    • Requirements
    • Installation Guide
      • Face Searching & Matching
        • Single Node
        • Clustering
      • Helmet Detection
      • Chicken Estimation
      • Face Detection
      • Over Dimension Over Load
      • Frontal License Plate Recognition
    • Analytics
      • Face Searching & Matching
        • Face Enrollment
          • Image Guideline
          • Face and Image Quality Assessment
            • Setup On Premise
            • API
          • Insert / Update / Delete Enrollment
          • Batch Enrollment
      • Helmet Detection
      • Chicken Estimation
      • People Demography
      • Face Detection
      • Over Dimension Over Load
      • License Plate Recognition -Frontal
    • Developer Guide
      • APIs
        • Face Searching & Matching
        • Helmet Detection
        • Chicken Estimation
        • People Demography
        • Face Detection
        • Over Dimension Over Load
        • Frontal License Plate Recognition
      • Vanilla APIs for Face Enrollment
      • Porting Enrollment Database Cluster to Docker
    • Changelogs
  • VisionAIre Dashboard
    • Introduction
    • Add Analytic Assignment
    • Accessing Vanilla Database
    • Connect to Vanilla Websocket
    • Create your own visualization
      • Migration from Old Streamer to New Streamer
      • Drawing Region of Interest
      • Additional Visualization Query
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  • Model Capability
  • Result Interpretation

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  1. Visionaire Stream
  2. Analytics
  3. Face Recognition
  4. Additional Information

Disclaimer

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Last updated 1 year ago

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Model Capability

  • Due to the capability of Face Extraction based on the Deep Learning models, it is highly dependent on the characteristics of the training data and the characteristics of the real case conditions when the model is applied. There are cases where Nodeflux Face Recognition model fails to generalize similar facial features. Similar to the human eye capability, on several occasions it is failed to distinguish two people who has similar characteristics at certain angles and lighting.

  • The face biometrics model has limited capability to distinguish identical twin cases.

Identical Twin Snapshot (source:
  • The angle and lighting condition on which the camera located may affect the result of face matching.

  • The face pose must be fully capture by the CCTV camera.

  • Sunglasses and other attributed that covering the face area may reduce the accuracy of the face Biometrics.

Result Interpretation

The accuracy of the Face Recognition model highly dependents on the captured. In small cases it may present False Positive result due to several conditions such as face attributes, angle and lighting.

Our NFV4-FR is designed to accurately identify human faces in images and videos. However, there may be rare cases where the model produces a false positive result if the image contains features that resemble a human face, such as an animal or an animated character. We strive to maintain a low error budget, but please be aware that there is a small possibility of false positive results occurring. If you have any concerns about the accuracy of the model, please don't hesitate to contact us for further assistance.

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Blurry Condition
Backlight or Low Light Condition
Object wear Sunglasses
Blurry Object Capture
False Positive Result