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How to Install face_recognition in Python: Solving Common Issues with dlib Installation

Facial recognition is an exciting technology used in security, apps, and even social media filters. Python provides a powerful library called face_recognition, built on top of another library called dlib, that makes facial recognition tasks easy. However, installing face_recognition and dlib can sometimes cause problems, especially for beginners.

In this blog, we will go through the step-by-step process of installing the face_recognition library and resolve common issues that can come up during the installation of dlib.

What is face_recognition?

The face_recognition library allows you to easily locate and identify faces in images or video using Python. It uses machine learning models from dlib to detect facial features, which is why dlib is essential for the library to work.

What is dlib?

dlib is a modern C++ library that provides tools for machine learning, computer vision, and image processing. It’s widely used for facial recognition because of its efficient facial landmark detection.

Step-by-Step Installation Guide for face_recognition

Prerequisites

Before installing face_recognition, you need to ensure that Python and some build tools are set up on your system.

For Windows:

  • Install Python 3.x from the official Python website.
  • Ensure you have the latest version of pip (pip is a package manager for Python).
 python -m pip install --upgrade pip

For macOS/Linux:

  • Python is often pre-installed, but you can update or install it from the terminal.
sudo apt-get update
sudo apt-get install python3 python3-pip  # For Linux

Once Python is installed, follow these steps to install face_recognition.

Step 1: Install CMake (Required by dlib)

dlib needs CMake to compile, so it’s crucial to have it installed before proceeding.

For Windows:

  • Download and install CMake from the official website. Make sure to select the option to add CMake to your system path during installation.

For macOS/Linux:

  • Install CMake using the terminal:
brew install cmake    # For macOS
sudo apt-get install cmake    # For Linux

Step 2: Install dlib

The dlib library can sometimes be tricky to install due to its compilation process, especially on Windows.

Windows:

  • Open the terminal (Command Prompt or PowerShell) as Administrator.
  • Run the following command:
pip install dlib

Note: If you face issues with this step, you can download the pre-built version of dlib for Windows:

  • Download from this unofficial page.
  • Install the .whl file using pip. For example:
pip install dlib‑19.22.99‑cp38‑cp38‑win_amd64.whl

macOS/Linux:

  • Install dependencies required for dlib:
sudo apt-get install build-essential cmake    # Linux
brew install cmake    # macOS
sudo apt-get install libopenblas-dev liblapack-dev    # For numerical computing support

Now, install dlib:

pip install dlib

Step 3: Install face_recognition

Once dlib is installed, installing face_recognition becomes straightforward.

Run the following command:

pip install face_recognition

Solving Common Issues with dlib Installation

Issue 1: dlib Failed to Install

One of the most common issues is the failure of dlib to install. This happens mostly because the C++ compiler and dependencies are not properly set up.

Solution for Windows:

  • Install Microsoft Visual C++ Build Tools:
  • Go to Microsoft’s website.
  • Download and install the build tools. Ensure the Desktop development with C++ option is selected.
  • After installation, try installing dlib again:
pip install dlib

Solution for macOS:

  • Install Xcode Command Line Tools:
  • Run the following command in the terminal:
xcode-select --install
  • Install Homebrew if you haven’t already:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
  • Use Homebrew to install required dependencies:
brew install cmake
brew install boost
  • After this, try installing dlib again.

Solution for Linux:

  • Install missing dependencies using the package manager:
sudo apt-get install build-essential cmake libboost-all-dev

Issue 2: CMake Error or Not Found

Sometimes, during the dlib installation, you might get an error saying CMake not found or something similar.

Solution:

  • Ensure that CMake is installed and available in your system path. You can check this by running:
cmake --version
  • If it shows an error, reinstall CMake and make sure it’s correctly added to your system path.

Issue 3: Memory Error During Compilation

When installing dlib, especially on low-powered systems (like older computers), you may encounter a memory error.

Solution:

  • You can try limiting the number of threads used during the compilation process to prevent memory overload. Use the following command:
pip install dlib --global-option=build_ext --global-option="-j 1"

Issue 4: Precompiled dlib for Windows

If you’re using Windows and still can’t install dlib despite trying all the steps, you can download precompiled dlib binaries from this unofficial source.

  • Go to the link and download the .whl file for your Python version and system architecture.
  • Use pip to install the .whl file:
pip install path_to_downloaded_whl_file.whl

Issue 5: Unsupported Python Version

dlib and face_recognition may not support all versions of Python.

Solution:

Check your Python version by running:

python --version

If it’s not Python 3.x, install Python 3 and switch to it. For example, install Python 3.8 or 3.9, which are widely supported:

sudo apt-get install python3.8

Testing the Installation

Once everything is installed, you can quickly test whether the face_recognition and dlib libraries are working.

Create a Python file (e.g., test_face_recognition.py) with the following code:

import face_recognition

# Load a sample image and get face encodings
image = face_recognition.load_image_file("sample_image.jpg")
face_locations = face_recognition.face_locations(image)

print(f"Found {len(face_locations)} face(s) in the image.")

Run this script:

python test_face_recognition.py

If everything is working, you should see output indicating how many faces were found in the image.

Conclusion

Installing face_recognition and dlib in Python can be tricky, but following this step-by-step guide should help you overcome common issues. Remember, the main challenges revolve around dlib’s compilation process, especially on Windows. However, with tools like CMake and proper dependencies, you’ll have everything set up in no time. Once installed, you can start building powerful facial recognition applications using Python!