Is Your Netatmo Camera's Person Detection Wrong?
One of the standout features of the Netatmo Smart Indoor and Outdoor Cameras is their ability to recognise faces and notify you when specific people arrive home. However, it can be frustrating and confusing when the system gets it wrong—mistaking your partner for a stranger or failing to recognise a family member at all.
This incorrect identification is rarely a fault with the camera itself. Instead, it's usually due to the quality of the data the camera's learning algorithm has to work with. By optimising a few key factors, you can significantly improve its accuracy and make the person detection feature truly reliable.
### How Netatmo's Face Recognition Learns
The camera learns what a person looks like by building a profile from the video clips it captures. Every time it sees a face, it compares it to the profiles it has stored. If the lighting is poor, the angle is bad, or the person is wearing a hat, the camera can get confused. The key to fixing wrong person detection is to help the camera build better, clearer profiles.
The main reasons for poor accuracy are:
- Weak Face Profiles: The stored images for a person are not clear enough.
- Poor Camera Placement: The camera's angle makes it difficult to see faces clearly.
- Challenging Lighting: Backlighting or shadows obscure facial features.
- Incorrect Training: The algorithm has been 'taught' incorrectly through mis-tagged events.
A Guide to Improving Person Detection Accuracy
Follow these steps to 'retrain' your camera and fix identification errors.
### 1. Curate and Improve Face Profiles
This is the most effective action you can take. You need to 'clean up' the reference photos the camera uses.
- Open the Netatmo Security App: Go to the settings menu (the gear icon) and select 'Face Recognition'.
- Select a Profile: Tap on the profile of the person who is frequently misidentified.
- Review the Photos: You will see all the little snapshots the camera has used to build this profile. Delete any bad photos. This includes pictures that are:
- Blurry or out of focus.
- Poorly lit or in deep shadow.
- Showing only the side of the face.
- Obscured by a hat, sunglasses, or a face mask.
- Taken from too far away.
- Add Good Photos: Ask the person to stand in front of the camera in good, even lighting, looking directly at it. This will allow the camera to capture a few high-quality, face-on images, which will dramatically strengthen their profile.
### 2. Optimise Camera Placement
The camera needs a clear view to work effectively.
- Ideal Height: The best position is typically around 2.5 to 3 metres (8-10 feet) from the ground.
- Correct Angle: The camera should be angled slightly downwards to capture the faces of people approaching it. If it's angled too sharply, it will only see the tops of their heads.
- Clear Line of Sight: Ensure there are no obstructions like tree branches or pillars blocking the view of the main pathways to your home.
### 3. Address Lighting Issues
Bad lighting is the enemy of face recognition.
- Avoid Backlighting: Try not to position the camera so that it points directly at a very bright area, like the rising or setting sun. This will cause faces to appear as dark silhouettes.
- Ensure Even Illumination: At night, make sure the area is well-lit, either by your porch light or the camera's own floodlight (for the Outdoor Camera). Deep shadows across a face can easily confuse the algorithm.
### 4. Diligently Correct Mistakes
The camera learns from your input.
- Identify 'Unknown Faces': When the app notifies you of an 'Unknown face', take the time to view the event and identify the person if you know them. This is how the camera learns new people and improves existing profiles.
- Correct Misidentifications: If the camera notifies you that 'John' has arrived, but the video clearly shows it's 'Jane', you can correct it. Long-press on the event in the timeline, and you should get an option to change the identification. Consistently correcting these errors will retrain the algorithm over time.