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Fixes (#18220)

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* Add option to not trim clip

* Improve API

* Update snapshot for new best objects

* Fix missing strings

* Convert to separate key

* Always include bounding box on snapshots

* improve autotracking relative zooming time calculation

* update proxy docs to note the need for comma separated header roles

* Add count translation

* tracked object lifecycle i18n fix

* update speed estimation docs

* clarity

* Re-initialize onvif information when toggling camera on live view

* Move time ago to card info and add face area

* Clarify face recognition docs

* Increase minimum face recognition area

* use clipFrom to in vod module endpoint to start at the correct time

* Cleanup media api

* Don't change duration

* Use search detail dialog for face library

* Move to segment based

* Cleanup

* Add back duration modification

* clean up docs

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Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.com>
This commit is contained in:
Nicolas Mowen
2025-05-14 16:44:06 -06:00
committed by GitHub
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commit d3d05fa397
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docs/docs/configuration/face_recognition.md
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@@ -105,19 +105,25 @@ When choosing images to include in the face training set it is recommended to al
:::
### Understanding the Train Tab
The Train tab in the face library displays recent face recognition attempts. Detected face images are grouped according to the person they were identified as potentially matching.
Each face image is labeled with a name (or `Unknown`) along with the confidence score of the recognition attempt. While each image can be used to train the system for a specific person, not all images are suitable for training.
Refer to the guidelines below for best practices on selecting images for training.
### Step 1 - Building a Strong Foundation
When first enabling face recognition it is important to build a foundation of strong images. It is recommended to start by uploading 1-5 photos containing just this person's face. It is important that the person's face in the photo is front-facing and not turned, this 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 front-facing. Ignore images from cameras that recognize faces from an angle.
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 front-facing. 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 over-fitting.
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 over-fitting.
Once a person starts to be consistently recognized correctly on images that are front-facing, it is time to move on to the next step.
You do not want to train images that are 90%+ as these are already being confidently recognized. In this step the goal is to train on clear, lower scoring front-facing images until the majority of front-facing images for a given person are consistently recognized correctly. Then it is time to move on to step 2.
### Step 2 - Expanding The Dataset
Once front-facing 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.
Once front-facing 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, and you still only want to train on images that score lower.
## FAQ