In this video you’ll learn how to build an offline face recognition pipeline to find faces on top of complex pictures. The full written explanation is available in the dedicated article

The pipeline will use:
Python and OpenCV to detect faces within complex pictures
Python and an OpenAI CLIP model to calculate the face embeddings
PostgreSQL and the pgvector extension to store the embeddings and calculate distance across them

Check out these resources to learn more:
Step by step article: https://aiven.io/developer/find-faces-with-pgvector
Pgvector blog post announcement: https://aiven.io/blog/aiven-for-postgres-supports-pgvector
OpenCV GitHub Repository: https://github.com/opencv/opencv/tree/master/data/haarcascades
OpenAI CLIP model: https://aiven.io/developer/find-faces-with-pgvector#:~:text=OpenAI%27s-,CLIP%20model,-via%20Hugging%20Face
PGVector: https://github.com/pgvector/pgvector

CHAPTERS

01:04 What are embeddings?

02:40 Embeddings in images

03:15 The face recognition pipeline steps

04:45 Prepare the PostgreSQL database

06:20 Create the face recognition pipeline

07:22 Detect faces with OpenCV

09:08 Calculate embeddings with imgembeddings and OpenAI CLIP model

10:48 Calculate image similarity using pgvector distance

11:46 Summary

Follow Francesco Tisiot on social media:
Twitter: https://twitter.com/FTisiot
LinkedIn: https://www.linkedin.com/in/francescotisiot/

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#imagerecognition #python #pythonprogramming #openai #opencv #opencvpython #postgresql #pgvector