Face recognition identifies known individuals by matching detected faces with previously learned facial data. When a known person is recognized, their name will be added as a `sub_label`. This information is included in the UI, filters, as well as in notifications.
Users running a Frigate+ model (or any custom model that natively detects faces) should ensure that `face` is added to the [list of objects to track](../plus/#available-label-types) either globally or for a specific camera. This will allow face detection to run at the same time as object detection and be more efficient.
Users without a model that detects faces can still run face recognition. Frigate uses a lightweight DNN face detection model that runs on the CPU. In this case, you should _not_ define `face` in your list of objects to track.
:::note
Frigate needs to first detect a `face` before it can recognize a face.
- **small**: Frigate will run a FaceNet embedding model to recognize faces, which runs locally on the CPU. This model is optimized for efficiency and is not as accurate.
- **large**: Frigate will run a large ArcFace embedding model that is optimized for accuracy. It is only recommended to be run when an integrated or dedicated GPU is available.
Face recognition is disabled by default, face recognition must be enabled in the UI or in your config file before it can be used. Face recognition is a global configuration setting.
Fine-tune face recognition with these optional parameters:
### Detection
-`detection_threshold`: Face detection confidence score required before recognition runs:
- Default: `0.7`
- Note: This is field only applies to the standalone face detection model, `min_score` should be used to filter for models that have face detection built in.
-`min_area`: Defines the minimum size (in pixels) a face must be before recognition runs.
- Default: `500` pixels.
- Depending on the resolution of your camera's `detect` stream, you can increase this value to ignore small or distant faces.
The number of images needed for a sufficient training set for face recognition varies depending on several factors:
- Diversity of the dataset: A dataset with diverse images, including variations in lighting, pose, and facial expressions, will require fewer images per person than a less diverse dataset.
- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
However, here are some general guidelines:
- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
## Creating a Robust Training Set
The accuracy of face recognition is heavily dependent on the quality of data given to it for training. It is recommended to build the face training library in phases.
:::tip
When choosing images to include in the face training set it is recommended to always follow these recommendations:
- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the model.
- Do not upload too many similar images at the same time, it is recommended to train no more than 4-6 similar images for each person to avoid overfitting.
When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 "portrait" photos for each person. It is important that the person's face in the photo is straight-on and not turned which will ensure a good starting point.
Then it is recommended to use the `Face Library` tab in Frigate to select and train images for each person as they are detected. When building a strong foundation it is strongly recommended to only train on images that are straight-on. Ignore images from cameras that recognize faces from an angle.
Aim to strike a balance between the quality of images while also having a range of conditions (day / night, different weather conditions, different times of day, etc.) in order to have diversity in the images used for each person and not have overfitting.
Once a person starts to be consistently recognized correctly on images that are straight-on, it is time to move on to the next step.
Once straight-on images are performing well, start choosing slightly off-angle images to include for training. It is important to still choose images where enough face detail is visible to recognize someone.
It is important to methodically add photos to the library, bulk importing photos (especially from a general photo library) will lead to overfitting in that particular scenario and hurt recognition performance.
### Why do unknown people score similarly to known people?
This can happen for a few different reasons, but this is usually an indicator that the training set needs to be improved. This is often related to overfitting:
- If you train with only a few images per person, especially if those images are very similar, the recognition model becomes overly specialized to those specific images.
- When you provide images with different poses, lighting, and expressions, the algorithm extracts features that are consistent across those variations.
- By training on a diverse set of images, the algorithm becomes less sensitive to minor variations and noise in the input image.
### I see scores above the threshold in the train tab, but a sub label wasn't assigned?
The Frigate face recognizer collects face recognition scores from all of the frames across the person objects lifecycle. The scores are continually weighted based on the area of the face, and a sub label will only be assigned to person if there is a prominent person recognized. This avoids cases where a single high confidence recognition result would throw off the results.
