Face detection: what is it and how does this tech work?

Table of Contents

What Is Face Detection?
How Does Face Detection Work?
Face Detection Methods
Feature-Based Method
Knowledge-Based Method
Template Matching Method
Appearance-Based Method
Face Detection Techniques
What Are the Challenges in Face Detection?
How Does Face Detection Work With Deep Learning?
Why Is Face Detection Important Today?
Face Detection vs. Face Recognition: What’s the Difference?
Advantages and Disadvantages of Face Detection Systems
Advantages of Face Detection
Disadvantages of Face Detection
Pros and Cons Table Summary
How Face Detection Algorithms are Used
Facial Motion Capture
Facial Recognition
Photography
Marketing
Emotional Inference
Lip Reading
Summary
Face Detection FAQ
How does face detection work?
How are faces detected?
What does face tracking mean?
Who uses face detection?
What is the difference between face detection and face recognition?

What Is Face Detection?

Face detection is AI-based computer technology that is used to extract and identify human faces from digital images. When integrated with biometric security systems (particularly, facial recognition ones), this kind of technology is what makes it possible to monitor and track people in real-time. In applications that use facial tracking, analysis, and recognition, face detection typically works as the first step and has a significant impact on how sequential operations within the app will perform.

Face detection helps with facial analysis by identifying the parts of a video or an image that should be focused on when determining gender, age, and emotions. Similarly, with facial recognition systems (which create “faceprint” maps of facial features), face detection data is included in the system’s algorithms. And why? Face detection helps determine which parts of the video or image are needed to produce a faceprint.

How Does Face Detection Work?

Face detection technology uses machine learning and algorithms in order to extract human faces from larger images; such images typically contain plenty of non-face objects, such as buildings, landscapes, and various body parts.

Facial detection algorithms usually begin by seeking out human eyes, which are one of the easiest facial features to detect. Next, the algorithm might try to find the mouth, nose, eyebrows, and iris. After identifying these facial features, and the algorithm concludes that it has extracted a face, it then goes through additional tests to confirm that it is, indeed, a face.

To make algorithms as accurate as possible, they must be trained with huge data sets that contain hundreds of thousands of images. Some of these images contain faces, while others do not. The training procedures help the algorithm’s ability to decide whether an image contains faces, and where those facial regions are located.

Also, now would be a good time to give you definitions of the main types of algorithms – ML, AI, and Deep Learning.

• Machine Learning (ML): ML algorithms use statistics to find patterns in huge amounts of data. This data can include words, numbers, images, clicks, and more. ML is the process behind many modern services – voice assistants (Siri and Alexa), search engines (Google and Baidu), and recommendation systems (Spotify and Netflix);
• Artificial Intelligence (AI): If an ML solution is programmed to learn how to perform a task, rather than just simple performance, then it is AI. Systems that use AI demonstrate behaviors similar to human intelligence – for instance, problem solving, planning, learning, perception, manipulation, and reasoning;
• Deep Learning: This algorithm is a subset of machine learning, and it is what forms deep neural networks; essentially, machines are given a greater ability to find and amplify tiny patterns. Such networks have any layers of computational nodes that collaborate to sift through data and deliver predictions.

Now, as for the exact technologies used to develop face detection applications; these include:

• OpenCV;
• Matlab;
• Tensorflow;
• Neural Networks.

All of these follow almost the exact same procedure for face detection.

Face Detection Methods

Three researchers from the University of California, David Kriegman, Ming-Hsuan Yang, and Narendra Ahuja, published a classification of facial detection methods. There are four classifiable categories, of which face detection algorithms can belong to 2+ groups. Let’s take a look at each category.

Feature-Based Method

This method located faces by extracting structural features. First, an algorithm is trained as a classifier. Next, it is used to sort facial regions from non-facial regions. The general idea is to move past humans’ instinctive knowledge of faces. When feature-based approaches tackle photos with many faces, they have a 94% success rate.

Summary: Features such as a person’s nose or eyes are used to detect a face.

Knowledge-Based Method

A knowledge-based algorithm is dependent upon a set of rules, and it is built on human knowledge. For instance, “rules” might include that a face should have eyes, a nose, and a mouth in certain positions relative to each other. However, this kind of method comes with one huge challenge: it is very difficult to build an appropriate rules set. If the rules are too general, there may be many false positives – and, conversely, if the rules are too detailed, the system could generate many false negatives.

Summary: A face is determined based on whether it meets a set of rules made by a human.

Template Matching Method

With a template matching algorithm, parameterized or pre-defined templates are used to locate or detect faces – the system measures the correlation between the input photos and the templates. For instance, the template may show that a human face is divided into nose, mouth, eyes, and face contour regions. Also, a facial model could be comprised of just edges and use the edge detection method – implementation of this approach is easy, but it is insufficient for face detection.

Summary: Images are compared to standard face patterns that have been previously stored.

Appearance-Based Method

An appearance-based algorithm uses a set of training images to “learn” what a face should look like. In general, this method relies on machine learning and statistical analysis to determine relevant facial characteristics. An appearance-based approach is generally considered to be stronger than the previously mentioned methods.

Summary: Statistical analysis and machine learning are combined to find a face image’s characteristics.

Face Detection Techniques

Some of the more specific facial detection techniques include:

1. Removing the background. Let’s say an image has a pre-defined, static background or a plain, single-color background – removing it can help determine the face’s boundaries;
2. With color images, the color of the skin can sometimes be used to find faces;
3. Motion can be used to detect faces. In a real-time video, a person’s face is nearly always in motion. However, a drawback of this technique is that a face could be confused with other moving objects.

When the aforementioned strategies are combined, they can create a comprehensive face detection approach.

What Are the Challenges in Face Detection?

Researchers Ashu Kumar, Amandeep Kaur, and Munish Kumar published a review of face detection techniques, which included a detailed explanation of the challenges that facial detection faces. To sum up their findings, the challenges in face detection include:

• Odd expressions. A human face might have an odd expression, making it difficult for facial detection algorithms to identify it as a face;
• Face occlusion. If a face is hidden by hair, a hat, a hand, glasses, or a scarf, it may result in a false negative;
• Illuminations. An image might not have uniform lighting effects; part of the image may be overexposed, while another part is very dark. Again, this can contribute to false negatives;
• Complex background. When lots of objects are present in an image, face detection’s accuracy is reduced;
• Too many faces. If there is a large number of human faces in an image, face detection software may have a hard time distinguishing between some of them;
• Low resolution. If an image’s resolution is poor, it is more difficult to detect faces;
• Skin color. If somebody’s skin color falls outside of the gradient that is recognized by the algorithm,
  their face might not be detected.

How Does Face Detection Work With Deep Learning?

As we mentioned earlier, deep learning is a subset of machine learning in which large neural networks process huge amounts of data and make complex predictions. So how does deep learning factor into face detection? Well, multiple deep learning methods have been developed specifically for facial detection.

One of the most popular deep learning approaches is the Multi-Task Cascaded Convolutional Neural Network – or, MTCNN. This approach is popular because it achieved cutting-edge results (for the time) on a variety of benchmark datasets – plus, it is able to use landmark detection to recognize the eyes, mouth, and other facial features.

MTCNN uses a cascade structure that contains three networks: P-net, R-Net, and O-Net. The image is first rescaled to different sizes (or an image period). P-Net proposes facial regions, R-Net filters the bounding boxes, and O-Net proposes facial landmarks.

Why Is Face Detection Important Today?

Face detection is the initial step in face analysis, face tracking, and, most importantly, face recognition. The latter industry is growing by leaps and bounds, and is applied to device unlocking, banking, hospitality, law enforcement, building security, and more. Face detection is necessary for facial recognition algorithms to know which parts of an image must be used to generate faceprints.

Face Detection vs. Face Recognition: What’s the Difference?

Facial recognition is merely one application of face detection. The former is used for biometric verification and device unlocking, whereas the latter can also be applied to facial analysis and tracking. For a more comprehensive look at face recognition, check out our Types of Biometrics guide.

Advantages and Disadvantages of Face Detection Systems

While face detection systems can be powerful, they are by no means foolproof, as demonstrated by our list of challenges. Let’s take a look at the advantages and disadvantages that face detection systems can bring.

Advantages of Face Detection

1. Better security. Face detection augments surveillance tactics and forms the basis of the identification process of terrorists and criminals;
2. Easy to integrate. Most face detection solutions are compatible with security software;
3. Automated identification. Face detection lets facial identification be automated, thus increasing efficiency alongside a heightened rate of accuracy.

Disadvantages of Face Detection

1. Huge storage requirements. Machine learning technology requires powerful data storage;
2. Detection can be vulnerable. We’ve outlined the way in which facial detection can be thrown off;
3. Potential privacy issues. There is disagreement on whether face detection is compatible with human privacy rights.

Pros and Cons Table Summary

How Face Detection Algorithms are Used

Before we wrap up this guide, we wanted to give some examples of how face detection algorithms are applied in the real world. Some applications include photography, lip reading, marketing, and more.

Facial Motion Capture

With applications such as Snapchat, people’s faces can be altered in real-time with fun filters. Facial detection makes this possible, as its algorithms tell the applications that there is a face that can be traced and changed.

Facial Recognition

Facial recognition adds increased security to nearly every global industry. It seeks to identify a person and then authenticate their identity – but for a person’s faceprint to be analyzed via facial recognition, the facial area to be assessed is determined by face detection.

Photography

Facial recognition can be used to “tag” people’s faces in photos across social media platforms, and facial detection forms the foundation of this application. Furthermore, facial detection technology can be used alongside tracking to focus on a person’s face while the photo is being taken.

Marketing

Facial surveillance can help stores determine customers that have visited a few times and offer them perks or discounts – thus fostering increased customer loyalty.

Emotional Inference

Emotion recognition applications are still in the works; when they are fully developed, AI might be able to “read” nonverbal cues, gestures, body movements, and facial expressions to convey a person’s feelings.

Lip Reading

The detection, modeling, and tracking of lips during videos can be used to generate automatic subtitles. Such an application can be found on YouTube, where some videos have the option to turn on subtitles, even if the creator has not provided any.

Summary

To sum up the key points of this guide:

• Face detection is AI-based computer technology that is used to extract and identify human faces from
  digital images;
• Face detection algorithms can be feature-based, knowledge-based, template matching, appearance- based, or a combination of methods;
• Advantages of face detection include better security, easy integration, and automated identification;
• Disadvantages include huge storage requirements, vulnerable detection, and potential privacy issues.

Face detection is the foundation of a huge number of facial applications – we can see it in our day-to-day life. When we unlock our smartphone via face recognition, that would not be possible without face detection. The same goes for facial recognition surveillance systems, photo tagging, and Snapchat filters. There are many exciting applications in the works that we can thank face detection for!

Face Detection FAQ

How does face detection work?

Face detection technology uses machine learning and algorithms in order to extract human faces from larger images; such images typically contain plenty of non-face objects, such as buildings, landscapes, and various body parts.

How are faces detected?

There are four methods used: features, knowledge, templates, or appearance.

What does face tracking mean?

Face tracking follows the presence of a face within a video frame.

Who uses face detection?

Face detection technology is used in many fields: entertainment, security, law enforcement, biometrics, and more.

What is the difference between face detection and face recognition?

Facial recognition is merely one application of face detection. The former is used for biometric verification and device unlocking, whereas the latter can also be applied to facial analysis and tracking.