How to Build Your Own Local Face Search Engine: A Comprehensive Guide

Imagine a world where you can search for faces in your photo collection just like you search for documents or emails. Sounds like science fiction? It’s not. With advancements in Artificial Intelligence and Machine Learning, building a local face search engine is now within everyone’s reach. This step-by-step guide will show you how to achieve this exciting feat.

Face Search Example — Sample demonstration of face recognition and search for the cast of “The Office”.

Understanding Face Recognition and Search

Face Matching, Embeddings, and Similarity Metrics

The main goal is to locate all instances of a given query face within a set of images. Instead of limiting the search to exact matches, we can sort results based on similarity scores, selecting only the top results or those above a certain similarity threshold.

Key Concept: Face Embeddings

An embedding is a representation of an input (like a face) in the form of a vector—a list of real-value numbers. These vectors capture the most essential features of the input, making it easier to compare their similarities or differences using distance metrics like cosine similarity or Euclidean distance.

In effective face recognition models, photos of the same person will have closely matching embeddings, while photos of different people will have different embeddings. This enables us to filter search results, focusing only on faces similar to the query face.

Similarity Scores Example — Example of matches sorted by similarity (descending). The first entry is the query face.

Implementing Your Local Face Search Engine

Step-by-Step Guide

To start, you’ll need a basic understanding of Python and a proper Python environment (version ≥3.10). For simplicity, we will utilize the Insightface library, which offers various face embedding models.

1. Setting Up

Begin by installing the required libraries. We recommend using a virtual environment for this purpose.

pip install numpy==1.26.4 pillow==10.4.0 insightface==0.7.3

2. Running the Script

Here’s a sample script to run a face search. Save it as run_face_search.py and run it from the command line:

python run_face_search.py -q "./query.png" -t "./face_search"

The query argument points to the image containing the query face, while the target argument points to the directory with the target images. Adjust the similarity threshold and minimum resolution parameters as needed.

Tips

Experiment with the similarity threshold to balance precision and recall. A higher threshold increases precision but may miss some matches, while a lower one ensures more matches but may introduce false positives.

Enhancing Performance

While the basic setup works well for small datasets, it may struggle with larger collections due to the need to recompute embeddings for each query. In a follow-up article, we’ll scale this approach using a vector database to optimize interfacing and querying.

Engage with Us

We hope this guide helps you build your own local face search engine. Have questions or want to share your experiences? Connect with us on Twitter, Threads, or see our works on Instagram.

This article was inspired by Becoming Human: Artificial Intelligence Magazine. Dive into the AI world for more exciting projects and insights!