No Facial Recognition
How Digeiz Understands Visitor Flows Without Facial Recognition
Digeiz uses clothing appearance to analyze the flow of visitors — not faces to recognize identities.
To understand how visitors move across a venue, Digeiz relies on visible clothing appearance cues such as shapes and colors. The face is excluded from this logic. The system is designed for flow analysis and aggregate insight, not for identifying or authenticating people.
Not Facial Recognition. Clothing Appearance.
Digeiz does not use the face to understand movement across a venue. Instead, the platform uses clothing appearance — the visible shapes and colors of what people wear — to connect observations across cameras and points of interest. This makes it possible to analyze visitor flows without turning the system into a face-based recognition tool.
Uses clothing appearance to understand visitor flows across spaces
Uses the face to recognize, authenticate, or identify a person
What Clothing Appearance Means
The Digeiz tag is built from visible clothing appearance cues, including shapes and colors observed across what a person is wearing. It does not rely on an exact body silhouette. It relies on how clothing appears in the image, including the way garments fall and structure a person's visible appearance.
- Visible clothing colors
- Visible clothing shapes
- Non-face visual cues
- Appearance continuity across spaces
analyzed
Built to Understand the Flow of Visitors
The purpose of the system is to understand how visitors move through a venue at scale: where flows build, how zones are connected, which paths are most used, and what patterns emerge over time. The platform is built to analyze population movement, not to inspect a specific individual.
Useful for Visitor Flows, Not for Identity
Digeiz is not designed for facial recognition, identity authentication, or named individual identification. The tag is built to describe clothing appearance for movement analysis across a venue, not to encode a unique identity. That is precisely what makes it useful for pathway analytics while remaining fundamentally different from a person recognition mechanism.
- ✕Facial recognition
- ✕Identity authentication
- ✕Named individual identification
- ✕Person-level surveillance
- Appearance can vary with clothing, angle or camera context
- Similar outfits can generate similar visual signals
- The system is designed for pathway analytics, not unique identity recognition
Video images are processed locally, in real time. The value delivered to clients comes from aggregated analytics — not from retaining raw visual content. How data is processed →