Skip to content

bigfunctions > generate_face_embedding

generate_face_embedding

Call or Deploy generate_face_embedding ?

✅ You can call this generate_face_embedding bigfunction directly from your Google Cloud Project (no install required).

  • This generate_face_embedding function is deployed in bigfunctions GCP project in 39 datasets for all of the 39 BigQuery regions. You need to use the dataset in the same region as your datasets (otherwise you may have a function not found error).
  • Function is public, so it can be called by anyone. Just copy / paste examples below in your BigQuery console. It just works!
  • You may prefer to deploy the BigFunction in your own project if you want to build and manage your own catalog of functions. This is particularly useful if you want to create private functions (for example calling your internal APIs). Discover the framework

Public BigFunctions Datasets:

Region Dataset
eu bigfunctions.eu
us bigfunctions.us
europe-west1 bigfunctions.europe_west1
asia-east1 bigfunctions.asia_east1
... ...

Description

Signature

generate_face_embedding(image_url)

Description

Detect Face on image and Generate its Embedding using deepface.

  • image_url is the url of a photo which contains a face. It can be a signed url of a cloud storage object. Then this function works well with object tables.
  • output is the output of DeepFace.represent method. It is like:
{
  embedding: [...],      # A 4096 float vector
  facial_areal: {...},   # Coordinated of detected face
  face_confidence: 1.0,  # Confidence score for face detection
}

Examples

Public test image from deepface

select bigfunctions.eu.generate_face_embedding('https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/img1.jpg')
select bigfunctions.us.generate_face_embedding('https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/img1.jpg')
select bigfunctions.europe_west1.generate_face_embedding('https://raw.githubusercontent.com/serengil/deepface/master/tests/dataset/img1.jpg')
+-------------------------------------------------------------------------+
| output                                                                  |
+-------------------------------------------------------------------------+
| {
  embedding: [...],
  facial_areal: {...},
  face_confidence: 1.0,
}
 |
+-------------------------------------------------------------------------+

Need help using generate_face_embedding?

The community can help! Engage the conversation on Slack

For professional suppport, don't hesitate to chat with us.

Found a bug using generate_face_embedding?

If the function does not work as expected, please

  • report a bug so that it can be improved.
  • or open the discussion with the community on Slack.

For professional suppport, don't hesitate to chat with us.

Use cases

This generate_face_embedding function is useful for facial recognition tasks within BigQuery. Here's a use case:

Scenario: You have a large dataset of images stored in Google Cloud Storage, represented as an object table in BigQuery. You want to identify all images that contain a specific person.

Implementation using generate_face_embedding:

  1. Pre-calculate the embedding of the target person: Take a known image of the target person and use generate_face_embedding to calculate their facial embedding. Store this embedding (a vector of numbers) somewhere accessible, like a small BigQuery table.

  2. Process your image dataset: Query your object table and apply generate_face_embedding to the image URL column for each row. This will generate facial embeddings for all faces detected in your dataset.

  3. Compare embeddings: Use a BigQuery function (e.g., a user-defined function or a built-in function for vector similarity like cosine similarity) to compare the embeddings generated in step 2 with the target person's embedding from step 1.

  4. Filter based on similarity: Filter the results based on a similarity threshold. Images with embeddings that are highly similar to the target person's embedding likely contain the target person.

Example SQL Snippet (Illustrative):

# Assuming 'target_embeddings' table contains pre-calculated embedding
# and 'image_table' is your object table with 'image_url' column

SELECT
    image_url
FROM
    image_table
WHERE EXISTS (
    SELECT
        1
    FROM
        target_embeddings
    WHERE
        cosine_similarity(bigfunctions.us.generate_face_embedding(image_table.image_url).embedding, target_embeddings.embedding) > 0.9  -- Example threshold
);

Benefits of using generate_face_embedding within BigQuery:

  • Scalability: BigQuery's distributed processing power allows you to analyze massive image datasets efficiently.
  • Integration: Seamlessly integrates with your existing BigQuery data and workflows. No need to export data or use external tools.
  • Cost-effectiveness: BigQuery's pricing model can be advantageous for large-scale processing compared to other solutions.

Other Use Cases:

  • Face clustering: Group similar faces together to identify different individuals in a dataset.
  • Security and surveillance: Identify known individuals in security footage.
  • Image search: Search for images containing similar faces.
  • Social media analysis: Analyze profile pictures for demographic information or to identify influencers.

Spread the word

BigFunctions is fully open-source. Help make it a success by spreading the word!

Share on Add a on