Troubleshooting Uniview (UNV) Facial Recognition Issues
Uniview's advanced facial recognition technology adds a powerful layer of intelligence to your surveillance system, allowing for smart alerts and quick footage retrieval based on specific individuals. However, when it doesn't work as expected, it can be a source of frustration.
This guide is designed to help you diagnose and solve common problems with Uniview's facial recognition, from poor detection and accuracy to issues with database management and alerts.
Prerequisite: System Capability Check
Before you begin, confirm that your hardware supports the feature.
- Camera: You need a Uniview IP camera that has the facial recognition or face capture feature built-in. Not all models do.
- NVR (Network Video Recorder): Your Uniview NVR must have the processing power and software to support facial recognition. Check your NVR's datasheet for the maximum number of channels it can analyse and the maximum size of the face database it can store.
Attempting to use this feature on incompatible hardware will not work.
Cause 1: Poor Camera Installation and Environment
The single biggest factor in facial recognition performance is the quality of the image the camera provides.
- Camera Angle and Height: The camera should be positioned to capture faces as directly as possible, not from a steep overhead angle. The ideal height is typically between 2.5 and 3.5 metres, with a slight downward tilt.
- Lighting: Strong backlighting (e.g., a camera pointing towards a bright window) will create silhouettes and make faces impossible to read. Ensure the target area is well and evenly lit. Infrared (IR) at night can work, but performance is generally better with good visible light.
- Obstructions: Part of the face being covered by hats, masks, hair, or deep shadows will significantly decrease accuracy.
- Distance: Faces that are too far away or too small in the frame cannot be analysed. Position the camera to cover a specific chokepoint, like a doorway or walkway, where people will pass at a predictable distance.
Cause 2: Incorrect NVR and Camera Configuration
The settings on both the camera and the NVR must be correctly configured to work together.
On the Camera's Web Interface:
- Log directly into the camera's IP address.
- Navigate to Smart → Face Detection.
- Ensure that Face Capture or a similar setting is enabled.
- Define the detection area. To improve performance, draw the detection box to cover only the area where you expect to see faces, excluding irrelevant background.
On the NVR's Interface:
- Navigate to Smart or VCA (Video Content Analysis).
- Select the correct camera channel.
- Choose Facial Recognition (note: this is different from Face Detection).
- Enable the feature for that channel.
- Configure Linkage Actions: This is a critical step. You must tell the NVR what to do when a face is recognised. This could be:
- Triggering an alarm output.
- Sending a push notification to your mobile app.
- Sounding a buzzer.
- Recording on specific channels.
Cause 3: Ineffective Face Database Management
The quality of your reference images is vital for matching accuracy.
- High-Quality Photos: When adding individuals to a face library (e.g., a 'Staff' or 'Banned' list), use clear, high-resolution, forward-facing photos. A passport-style photo is ideal.
- Avoid Poor Images: Do not use images that are blurry, poorly lit, taken from an angle, or where the person is wearing sunglasses or a hat.
- Multiple Libraries: Use different libraries for different groups (e.g., Whitelist, Blacklist, VIPs). Ensure you apply your recognition rules to the correct library.
By optimising the camera's physical placement, carefully configuring the software settings, and maintaining a high-quality face database, you can significantly improve the reliability and accuracy of your Uniview facial recognition system.