A Walk through Top Python Libraries for Face Analysis


Over the past couple of decades, people have envisioned developing machines endowed with human characteristics. They’ve dreamt about building machines that have the ability to reason, learn from past experience, and exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human. One of the most exciting developments is that computers now have the capability to see and understand visual data.

Nowadays, computer vision outperforms human vision in some areas—for instance, when discerning anomalies in medical images. Computer vision technology has recently made a big leap forward. It has become an integral part of our day-to-day lives due to the advancements in artificial intelligence and the rise in computing power.

What is computer vision?

Computer vision is an artificial intelligence field that revolves around training machines to see, interpret, and understand visual data. Using digital images from cameras and videos, and leveraging the power of various deep learning models, machines can accurately identify and classify objects. If necessary, they can react based on what they see.

The concept of computer vision tends to mimic the workings of the human brain, training machines to process an image at pixel level and interpret it. To get an idea of how this works, let’s look at the picture of Abraham Lincoln displayed below.

Every pixel’s brightness is rendered as a single 8-bit number within this picture, varying from 0 (black) to 255 (white). For the sake of simplicity, we’ve selected the original image in a black-and-white format. The mapped and pixelated version of the image shows the respective value of each pixel—again, 0 represents black and 255 represents white. These values are fed as input into an appropriate computer vision algorithm that handles the analysis and decision-making.
The original Image
The pixelated Image

Common computer vision applications

According to a newly released report by Tractica, the global market for computer vision technology will reach $48.6 billion annually by 2022. The core concepts of computer vision have already been integrated into many products that we come across in our daily lives. Some examples are listed below:

  • Facial recognition plays a vital role in this field by enabling computers to link the images of people’s faces to their identities.
  • Self-driving cars can recognize their surroundings through computer vision.
  • Healthcare is improved because computer vision helps locate cancerous moles on skin images.
  • Augmented reality (AR) allows applications to detect physical objects in real time and use that information to create an interactive experience.

Why Python is ideal for computer vision

Python is simple, easy-to-learn, and enables both new and seasoned programmers to easily transform their ideas into code. Frequently labeled a batteries-included language, Python is one of the most widespread, mature, and well-supported languages in machine learning. That’s why programmers like to use Python when working on computer vision projects.

The following Python attributes make it a perfect fit for computer vision projects:

  • It is open source.
  • It offers easy coding.
  • Prototyping is fast.
  • It’s portable across different platforms and environments.
  • There are versatile and well-integrated libraries for machine learning.
  • Web development frameworks like Flask and Django are well supported.

It’s worth noting that automatic face analysis has a very strong presence in computer vision. It’s featured in multiple applications, like visual surveillance, biometric identification, video conferencing, human-machine interaction, and content-based image retrieval.

Top Python libraries for face analysis

Python Libraries are a set of workable modules and functions that reduce the use of code in our day-to-day life and play a vital role in simplifying our programming experience. While numerous Python libraries deal with facial analysis, we’ll go through the most useful and handy ones, which will constitute the foundation of the utilities that we aim to develop throughout this course.

While exploring these Python libraries, we’ll shed light on their statistics, collected from Github and mainly from public repositories. These statistics will take into consideration the following metrics:

  • Stars indicate the level of appreciation of the project.
  • Forks reveal the number of copies of the project repository to introduce enhancements if possible.
  • Releases denote the level of contribution to the designated project in some cases.

Let’s start with the top-notch libraries.


MediaPipe is a free, open-source framework developed by Google. It offers cross-platform, cutting-edge machine learning (ML) solutions for live and streaming media. Some examples are as follows:

  • Face detection
  • Face mesh
  • Object detection
  • Hand Tracking

MediaPipe, in summary, contains the following metrics:




Latest Release






C++ (78.8%)

Starlark (8.1%)

Java   (5.8%)

Python (4.0%)


Dlib is an open-source toolkit developed in C++ and featuring various ML algorithms that solve real-life problems. It’s used in a broad range of domains from robotics to embedded devices and mobile phones.




Latest Release









DeepFace is a deep learning face recognition system developed by a Facebook research group. It identifies human faces in digital images and was trained using four million images. DeepFace is a hybrid face recognition framework, wrapping state-of-the-art models like VGG -Face, Google, and FaceNet among others.




Latest Release







Jupyter Notebook (16.0%)

This list of libraries is dynamic, not exhaustive, and may vary depending on the future releases of the cited libraries or new arrivals within this category.


These libraries are extremely valuable when running face analysis tasks because they save time and provide explicit functions that can be built upon. Many Python libraries provide a variety of functions to analyze faces. We’ve this section provides a short list of some of them here for further elaboration across a wide range of functionalities to be approached later in our course.