Face Recognition Python: Popular Techniques and Libraries

by | Sep 20, 2018 | Data Science

9 Min Read. |

Face Recognition Python is the latest trend in Machine Learning techniques. OpenCV, the most popular library for computer vision, provides bindings for Python. OpenCV uses machine learning algorithms to search for faces within a picture.

In this discussion we will learn about Face Recognition using Python, exploring face recognition Python code in detail. Though new, Face Recognition Python code is a very popular concept. It is interesting to know about the different ways of face detection using Python.

Once you have a basic understanding of facial recognition using Python, you can delve deeper into the cascade of classifiers for advanced techniques in facial recognition using Python. You will also gain knowledge about the popular libraries for facial recognition using Python.

Face _Recognition

What is Face Recognition?

Facial Recognition is a category of biometric software that maps an individual’s facial features mathematically and stores the data as a faceprint. The software uses Deep Learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual’s identity.

Face Recognition

High-quality cameras in mobile devices have made facial recognition a viable option for authentication as well as identification. Apple’s iPhone X, for example, includes Face ID technology that lets users unlock their phones with a faceprint mapped by the phone’s camera.

The phone’s software, which is designed with 3-D modeling to resist being spoofed by photos or masks, captures and compares over 30,000 variables. Face ID can also be used to authenticate purchases with Apple Pay and in the iTunes Store, App Store, and iBooks Store. Apple encrypts and stores faceprint data in the cloud, but authentication takes place directly on the device.

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Software Developers can use Amazon Rekognition, an image analysis service that’s part of the Amazon AI suite, to add facial recognition and analysis features to an application. Google provides a similar capability with its Google Cloud Vision API.

The technology, which uses machine learning to detect, match and identify faces, is being used in a wide variety of ways, including entertainment and marketing. The Kinect motion gaming system, for example, uses facial recognition to differentiate among players. Smart advertisements in airports are now able to identify the gender, ethnicity and approximate age of a passerby and target the advertisement to the person’s demographic.

Face Recognition using Python

Faces are made of thousands of fine lines and features that must be matched. The face recognition using Python, break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to face Recognition Python is the latest trend in Machine Learning techniques. OpenCV uses Machine Learning algorithms to search for faces within a picture.

Python Image


Facial Recognition using Python Libraries

The most popular and probably the simplest way to detect faces using Python is by using the OpenCV package. Originally written in C/C++, OpenCV now provides bindings for Python.

It uses machine learning algorithms to search for faces within a picture. Faces are very complicated, made of thousands of small patterns and features that must be matched. The face recognition algorithms break the task of identifying the face into thousands of smaller, bite-sized tasks, each of which is easy to solve, known as classifiers.

A face may have 5000 or more classifiers, all of which must match for a face to be detected. Since there are at least 5,000 or more tests per block, you might have millions of calculations to do, which makes it a difficult process. To solve this, OpenCV uses cascades.

The OpenCV cascade breaks the problem of detecting faces into multiple stages. It performs a detailed test for each block. The algorithm may have 30 to 50 of these stages or cascades, and it will only detect a face if all stages pass.

The cascades are a bunch of XML files that contain OpenCV data used to detect objects. You initialize your code with the cascade you want, and then it does the work for you. Since face detection is such a common case, OpenCV comes with a number of built-in cascades for detecting everything from faces to eyes to hands to legs.

You may use other alternatives to OpenCV, like dlib – that come with Deep Learning based Detection and Recognition models.

dlib as a code does the following:

  • Use MMOD (Deep Learning) algorithm to find face bounding boxes
  • Find facial landmark points (like eyes, nose, etc.)
  • Use the points to realign the face crops so that it is frontal.
  • Use a Deep Learning model to calculate embeddings from the face crop.

These embeddings are 128-dimensional vectors. Their nature is such that the same faces will end up closer to each other while different faces will end up far apart.

Also, you may use Dlib face detector in place of OpenCV. Then you can use Pre-trained model like from Facenet, to extract the feature from the face and create embedding for each unique face and assign a name to it. Both Dlib and Facenet score well on accuracy meter.

Read my earlier post on top 10 Python Libraries.

Face Recognition using Python Algorithm

Face Recognition using Python and OpenCV follows a well-defined pattern. When you meet someone for the first time in your life, you look at his/her face, eyes, nose, mouth, color, and overall features. This is your mind learning or training for the face recognition of that person by gathering face data.

Then the person tells you his/her name. At this point, your mind knows that the face data it just learned belongs to the person. Now, your mind is trained and ready to do face recognition. Next time when you will see the person or his/her face in a picture you will immediately recognize.

This is how Face Recognition works. The more you will meet, the more data your mind will collect about the person and the better you will become at recognizing him/her.

The coding steps for face recognition are the same as we discussed it in real life example above.

  • Training Data Gathering: Gather face data (face images in this case) of the persons you want to recognize
  • Training of Recognizer: Feed that face data (and respective names of each face) to the face recognizer so that it can learn.
  • Recognition: Feed new faces of the persons and see if the face recognizer you just trained recognizes them.

OpenCV provides the following three face recognizers:

  1. Eigenface recognizer
  2. Fisherfacerecogniser
  3. LBPH face recognizer

Eigenface Recognizer:

Eigenface Recognizer algorithm considers the fact that not all parts of a face are equally important and equally useful. When we look at someone we recognize the person his distinct features like eyes, nose, cheeks, forehead and how they vary with respect to each other.

The idea is to actually focus on the areas of maximum change (mathematically speaking, this change is variance) of the face. For example, from eyes to nose there is a significant change and the same is the case from nose to mouth.

When you look at multiple faces you compare them by looking at these parts of the faces because these parts are the most useful and important components of a face. Important because they catch the maximum change among faces, change the helps you differentiate one face from the other.

Eigenfaces_face recognition

EigenFacesFace Recognizer looks at all the training images of all the persons as a whole and tries to extract the components which are important and useful (the components that catch the maximum variance/change) and discards the rest of the components.

These important components it extracts are called principal components. EigenFaces Face Recognizer trains itself (by extracting principal components). Another point to be considered is that it also keeps a record of which principal component belongs to which person.

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FisherFaces Face Recognizer:

FisherFaces Face Recognizer as a face recognition algorithm is an improved version of EigenFaces. It looks at all the training faces of all the persons at once and finds principal components from all of them combined. By capturing principal components from all the of them combined you are focusing on the features that represent all the persons in the training data.

The FisherFaces Face Recognizer approach has multiple drawbacks, for example, images with sharp changes (like light changes which are not a useful feature at all) may dominate the rest of the images and one may end up with features that are from an external source like light and are not useful for discrimination at all. Eventually, your principal components will represent light changes and not the actual facial features.

Fisherfaces_face recognition

Also, Fisherfaces algorithm, instead of extracting useful features that represent all the faces of all the persons, extracts useful features that discriminate one person from the others. This way features of one person do not dominate over the others and you have the features that discriminate one person from the others.

Local Binary Patterns Histograms (LBPH) Face Recognizer

Both Eigenfaces and Fisherfaces are affected by light and in real life, perfect light conditions are not always available. LBPH face recognizer is an improvement to overcome this drawback.LBPH algorithm tries to find the local structure of an image and it does that by comparing each pixel with its neighboring pixels.

With so much just on the horizon, it will be interesting to see where this rise in Facial Recognition technology takes us.

LPB recognition

Face recognition is fast gaining importance in various fields. We have entered an age when Facial Recognition technologies will soon be part of everyday life. China, for example, monitors by CCTV or by police wearing special glasses and then logs onto a database that checks on the habitual behavior of the people, their social credit and even their friends.

Cameras and facial recognition are increasingly being used in public and private buildings. Some schools in the United States are now installing facial recognition systems, to prevent gun attacks by students, given that most rampages are carried out by students whose faces will already be on a database and have full access to the premises. This has led to increased demand for coders and developers with knowledge of Face Recognition algorithms; Python and OpenCV, in particular.

Face Recognition with Python takes just a few lines of code to have a fully working face recognition application and you have the option of switching between face recognizers with a single line of code change. You can come up with detailed codes with a simple approach, and what more, a much better outcome.

Mastering Python for face recognition or otherwise will prepare you better for a rewarding career in Python. Tremendous growth, enormous learning, and lucrative salary are some of the well-known perks of a promising career in Python. Add to that the magic touch of a Data Analytics course, and you are ready to rock!

Python career also offers diversity in terms of career choices. One can start off as a developer or programmer and later switch to the role of a data scientist. With a substantial amount of experience and Python online course certification, one can also become a certified trainer in Python or an entrepreneur. But the bottom line remains the same.

Read my earlier post on career opportunities in Python.

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