While DISPL’s face recognition technology excels in dynamic environments—like store aisles, corridors, or kiosks where visitors pass by or pause briefly—it can run into complications when installed in waiting areas where people remain seated or stationary for extended periods. Below is an overview of why waiting areas pose challenges and what factors contribute to less reliable results.
1. Long Dwell Times and Micro-Movements
In a waiting area, individuals often:
- Sit for extended periods, looking around, adjusting their posture, or chatting.
- Frequently change their head angles—turning sideways or looking down at a phone.
Why This Causes Problems
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Constant Descriptor Updates
- Our system tracks a face by continuously updating a “face descriptor” (the set of key facial points). If someone remains in view for a very long time and keeps shifting angles, the algorithm may interpret a single individual’s multiple partial angles as different faces.
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Risk of Splitting or Reassigning IDs
- Repeated partial views (left profile, right profile, partial face) can lead to repeated “new face” detections or lost tracks. Once the face reappears at a different angle, the system might not realize it’s the same person if the face was never fully frontal.
2. Partial and Half-Face Views
Waiting areas often involve:
- Chairs or benches positioned so people are not facing the camera directly.
- Visitors reading, looking at their phones, or talking to someone beside them—exposing mostly a profile or half-face to the camera.
Why This Causes Problems
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High Sensitivity vs. Standard Sensitivity
- High Sensitivity mode captures more partial faces but can also generate false positives or assign multiple IDs to the same person.
- Standard Sensitivity (which ignores steep angles) may fail to detect people who never look fully forward, resulting in fewer recognized faces.
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Frequent Face Occlusions
- In some waiting areas, individuals wear headphones, cover their faces while resting, or hold items (e.g., a phone) close to the head. These partial occlusions make accurate descriptor matching even harder.
3. Inconsistent Session Tracking
Our system typically expects shorter interactions (2–5 seconds or slightly more). In a waiting area, someone might remain in front of the camera for several minutes or even hours, which can lead to:
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Session Fragmentation
- If the camera briefly loses sight of the face (due to a turn of the head or because the person leaves and returns), the system may generate separate sessions for what’s actually the same individual.
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Extended Inactivity
- Long dwell times can cause timing logic (e.g., how the software groups a person’s visit into a “session”) to behave in unexpected ways, especially if the device goes offline or if there are network disruptions.
4. Ideal Use Cases vs. Waiting Areas
DISPL’s audience detection is best suited for:
- Walk-by or short pause scenarios: Store aisles, checkouts, kiosks, corridors.
- Environments where visitors naturally face the camera at a comfortable angle (±30°).
In a waiting area, visitors often won’t look directly into the camera, or they might remain in partial profile for prolonged periods—both of which reduce recognition accuracy and session stability.
5. Potential Workarounds (When Waiting Areas Are Unavoidable)
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Adjust Camera Position
- Angle the camera more frontally, ensuring that if a person looks toward a central screen or queue display, they’ll briefly present a frontal face.
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Optimize Sensitivity Settings
- Discuss advanced parameter tuning with your DISPL technical contact (e.g., narrower angle thresholds or extended session times).
- Be aware that enabling High Sensitivity can boost detection of partial faces but increases the risk of multiple IDs for the same person.
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Short Sessions
- If your waiting area also has light foot traffic, you can maintain standard session lengths, understanding that fully stationary individuals may not always be recognized accurately.
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Periodic Training for Staff
- If staff or regular visitors occupy the space, consider instructing them on how the camera works (e.g., being aware of obstructing movements or half-face placements).
Note: Even with these mitigations, waiting areas remain the least ideal scenario for face recognition due to inherently longer dwell times and partial angles. Expect lower match precision compared to areas where visitors engage in shorter, more direct interactions.
6. Key Takeaways
- Long Durations & Shifting Postures: People in waiting areas introduce many partial or changing face angles, confusing the AI’s tracking.
- Repeated Occlusions: Half-faces, headphones, or turning away can cause repeated new-face detections.
- Less Reliability vs. High-Traffic Zones: Our solution is optimized for environments where visitors briefly face the camera (2–5 seconds). Waiting rooms inherently deviate from this model.
If you must install in a waiting area, manage expectations about potential lower accuracy. For more detailed guidance on configuration adjustments, please reach out to DISPL support or refer back to our other articles on camera placement and environment best practices.
Further Reading
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Article 1: Overview & Principles
Understand the AI basics—why angles and dwell times matter. -
Article 2: Good & Bad Cases, Common Pitfalls
See real examples of optimal vs. problematic camera setups. -
Article 3: Installation & Setup Checklist
Follow a step-by-step plan for mounting, configuring, and testing your cameras.
Still have questions? Contact our DISPL technical support team for expert advice on balancing waiting-area constraints with the best possible face recognition performance.
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