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* make semantic search optional * config * frontend metrics * docs * tweak * fixes * also check genai cameras for embeddings context
57 lines
3.3 KiB
Markdown
57 lines
3.3 KiB
Markdown
---
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id: face_recognition
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title: Face Recognition
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---
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Face recognition allows people to be assigned names and when their face is recognized Frigate will assign the person's name as a sub label. This information is included in the UI, filters, as well as in notifications.
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Frigate has support for CV2 Local Binary Pattern Face Recognizer to recognize faces, which runs locally. A lightweight face landmark detection model is also used to align faces before running them through the face recognizer.
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## Configuration
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Face recognition is disabled by default, face recognition must be enabled in your config file before it can be used. Face recognition is a global configuration setting.
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```yaml
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face_recognition:
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enabled: true
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```
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## Dataset
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The number of images needed for a sufficient training set for face recognition varies depending on several factors:
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- 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.
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- Desired accuracy: The higher the desired accuracy, the more images are typically needed.
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However, here are some general guidelines:
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- Minimum: For basic face recognition tasks, a minimum of 10-20 images per person is often recommended.
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- Recommended: For more robust and accurate systems, 30-50 images per person is a good starting point.
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- Ideal: For optimal performance, especially in challenging conditions, 100 or more images per person can be beneficial.
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## Creating a Robust Training Set
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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.
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:::tip
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When choosing images to include in the face training set it is recommended to always follow these recommendations:
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- If it is difficult to make out details in a persons face it will not be helpful in training.
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- Avoid images with under/over-exposure.
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- Avoid blurry / pixelated images.
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- Be careful when uploading images of people when they are wearing clothing that covers a lot of their face as this may confuse the training.
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- Do not upload too many images at the same time, it is recommended to train 4-6 images for each person each day so it is easier to know if the previously added images helped or hurt performance.
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:::
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### Step 1 - Building a Strong Foundation
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When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-2 photos taken by a smartphone 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.
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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. 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.
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### Step 2 - Expanding The Dataset
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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.
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