Is Your I Pro System Failing to Recognise Familiar Faces?
Facial recognition is a powerful feature in modern I Pro security systems, offering the ability to distinguish between family members and strangers, and providing smarter, more personalised notifications. However, when the system fails to recognise someone, misidentifies them, or misses them entirely, it can be both frustrating and a security concern.
Achieving high accuracy with facial recognition isn't just about the software; it's heavily dependent on the physical setup of your cameras and the quality of the data you provide. This guide will walk you through the key factors that influence performance and how to optimise your system for reliable facial recognition.
Step 1: Build a High-Quality Face Library
The foundation of any facial recognition system is its database of known faces. A poor-quality library will lead to poor results.
- Use Clear, Well-Lit Photos: When adding a person to your library, use a clear, front-on photo where their face is well-illuminated. Avoid photos with harsh shadows, sunglasses, or hats.
- Add Multiple Angles: Don't rely on a single photo. The best systems allow you to add multiple images for each person. Add photos from the front, the left side, and the right side.
- Vary the Images: Include images with and without glasses if the person wears them. Add photos from slightly different lighting conditions. The more varied, high-quality data the AI has to learn from, the better it will be at identifying that person in real-world conditions.
Step 2: Optimise Camera Placement and Angle
Where you place your camera is the most critical factor for accurate facial recognition. A camera positioned for a general overview is often poorly positioned for identifying faces.
The Ideal Placement:
- Height: The camera should be mounted as close to eye level as possible, typically between 4.5 and 5.5 feet (around 1.4 to 1.7 metres) from the ground. Cameras mounted high up under the eaves are terrible for facial recognition as they mostly see the tops of heads.
- Angle: The camera should face approaching visitors head-on. A sharp side-angle view of a face is much harder for AI to analyse.
- Distance: Facial recognition works best when a person's face occupies a significant portion of the frame. Position the camera to capture faces at a specific choke-point, like a doorway or the start of a path.
Step 3: Control the Lighting Conditions
Lighting can make or break facial recognition performance.
- Avoid Backlighting: This is the number one lighting problem. If a strong light source (like the sun) is behind the person, their face will be cast in shadow, creating a silhouette. The camera sensor will be unable to see their facial features. You must ensure the primary light source is in front of the person, illuminating their face.
- Ensure Sufficient Light: The area needs to be bright enough for the camera to capture a clear, detailed image without digital noise. If the area is too dark, the image will be grainy and unsuitable for analysis.
- Use Soft, Even Lighting: Diffused, even lighting is ideal. A single, harsh spotlight can create strong shadows that obscure facial features.
Step 4: Keep Your System and Software Updated
Manufacturers are constantly improving their AI algorithms. The facial recognition you get from a firmware update six months from now might be significantly more advanced than what you have today.
- Enable Automatic Updates: Ensure your I Pro recorder (NVR/DVR) and cameras are set to receive firmware updates automatically.
- Update Your Software/App: Regularly check for updates to the I Pro viewing software or mobile app you use. These updates often contain performance improvements and new features.
Step 5: Understand the Limitations
It's important to have realistic expectations. No facial recognition system is perfect.
- Obstructions: Hats, hoods, sunglasses, and face masks can all prevent the system from getting a clear enough view.
- Speed of Movement: Someone walking quickly past a camera may not give the system enough time to capture a high-quality image for analysis.
By carefully considering these factors and making strategic adjustments, you can dramatically improve the accuracy and reliability of your I Pro facial recognition system.