generate_face_embedding.md

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.