Face Detection: What Is It, How Does This Tech work And What Is It Used For?
Facial recognition is a new technology that’s being built into all sorts of applications, from airport surveillance kiosks to social media engines.
It’s also one of the more controversial technologies being pioneered today, as it sets up deep questions regarding security versus privacy rights, and how these facial recognition applications can be safely and fairly applied.
Facial recognition is also known as face recognition or face detection.
We can even detect ghosts Hahahah !
What Is Face Detection?
Face detection — also called facial detection — is an artificial intelligence (AI) based computer technology used to find and identify human faces in digital images. Face detection technology can be applied to various fields — including security, biometrics, law enforcement, entertainment and personal safety — to provide surveillance and tracking of people in real time.
Face detection has progressed from rudimentary computer vision techniques to advances in machine learning (ML) to increasingly sophisticated artificial neural networks (ANN) and related technologies; the result has been continuous performance improvements. It now plays an important role as the first step in many key applications — including face tracking, face analysis and facial recognition. Face detection has a significant effect on how sequential operations will perform in the application.
In face analysis, face detection helps identify which parts of an image or video should be focused on to determine age, gender and emotions using facial expressions. In a facial recognition system — which maps an individual’s facial features mathematically and stores the data as a faceprint — face detection data is required for the algorithms that discern which parts of an image or video are needed to generate a faceprint. Once identified, the new faceprint can be compared with stored faceprints to determine if there is a match.
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.
The methods used in face detection can be knowledge-based, feature-based, template matching or appearance-based. Each has advantages and disadvantages:
- Knowledge-based, or rule-based methods, describe a face based on rules. The challenge of this approach is the difficulty of coming up with well-defined rules.
- Feature invariant methods — which use features such as a person’s eyes or nose to detect a face — can be negatively affected by noise and light.
- Template-matching methods are based on comparing images with standard face patterns or features that have been stored previously and correlating the two to detect a face. Unfortunately these methods do not address variations in pose, scale and shape.
- Appearance-based methods employ statistical analysis and machine learning to find the relevant characteristics of face images. This method, also used in feature extraction for face recognition, is divided into sub-methods.
Some of the more specific facial detection techniques include:
- 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;
- With color images, the color of the skin can sometimes be used to find faces;
- 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.
Advantages of face detection
As a key element in facial imaging applications, such as facial recognition and face analysis, face detection creates various advantages for users, including:
- Improved security. Face detection improves surveillance efforts and helps track down criminals and terrorists. Personal security is also enhanced since there is nothing for hackers to steal or change, such as passwords.
- Easy to integrate. Face detection and facial recognition technology is easy to integrate, and most solutions are compatible with the majority of security software.
- Automated identification. In the past, identification was manually performed by a person; this was inefficient and frequently inaccurate. Face detection allows the identification process to be automated, thus saving time and increasing accuracy.
Disadvantages of face detection
While face detection provides several large benefits to users, it also holds various disadvantages, including:
- Massive data storage burden. The ML technology used in face detection requires powerful data storage that may not be available to all users.
- Detection is vulnerable. While face detection provides more accurate results than manual identification processes, it can also be more easily thrown off by changes in appearance or camera angles.
- A potential breach of privacy. Face detection’s ability to help the government track down criminals creates huge benefits; however, the same surveillance can allow the government to observe private citizens. Strict regulations must be set to ensure the technology is used fairly and in compliance with human privacy rights.
Face detection vs. face recognition
Although the terms face detection and face recognition are often used together, facial recognition is only one application for face detection — albeit one of the most significant ones. Facial recognition is used for unlocking phones and mobile apps as well as for Biometric verification. The banking, retail and transportation-security industries employ facial recognition to reduce crime and prevent violence.
In short, the term face recognition extends beyond detecting the presence of a human face to determine whose face it is. The process uses a computer application that captures a digital image of an individual’s face — sometimes taken from a video frame — and compares it to images in a database of stored records.
Uses of face detection
Although all facial recognition systems use face detection, not all face detection systems are used for facial recognition. Face detection can also be applied for facial motion capture, or the process of electronically converting a human’s facial movements into a digital database using cameras or laser scanners. This database can be used to produce realistic computer animation for movies, games or avatars.
Face detection can also be used to auto-focus cameras or to count how many people have entered an area. The technology also has marketing applications — for example, displaying specific advertisements when a particular face is recognized.
Another application for face detection is as part of a software implementation of emotional inference, which can, for example, be used to help people with autism understand the feelings of people around them. The program “reads” the emotions on a human face using advanced image processing.
An additional use is drawing language inferences from visual cues, or “lip reading.” This can help computers determine who is speaking, which may be helpful in security applications. Furthermore, face detection can be used to help determine which parts of an image to blur to assure privacy.
Emerging Applications Of Augmented Reality (AR) Face Recognition
- AR Advertising
AR advertising is a new and exciting way for brands and consumers to communicate, allowing potential customers to create a personal experience that is meaningful and memorable. Event advertising could be enhanced by AR integrated on websites and in apps with interactive formats e.g. turn yourself into a movie character or have fun with branded face filters. These are far more compelling than traditional ad formats. Placing customers at the heart of an advert makes them more likely to share a photo or video with friends and to capture the attention of other potential shoppers which increases brand audiences.
- Retail
AR face recognition for virtual try on technology recreates products realistically, with natural colours and textures, lighting and physical form — how a fabric drapes, a hat sits or a necklace hangs on the neck. Customers get a realistic sense of how they would look in an item. This could hugely reduce the number of returned items, saving companies time and money.
- Online Dating
AR technology can allow users to hold video chats with one another as avatars, offering a discreet and secure initial ice-breaker and an environment in which safety and trust can be established and strengthened.
- Transport
Passengers in autonomous vehicles wearing AR head-up displays (HUDs) could be alerted to obstacles, traffic conditions and route navigation. Cameras running augmented reality face recognition and analytics technologies could monitor drivers for signs of tiredness and advise them to take a break.
What the Future Holds?
The future of facial recognition technology is bright. Forecasters opine that this technology is expected to grow at a formidable rate and will generate huge revenues in the coming years. Security and surveillances are the major segments which will be deeply influenced. Other areas that are now welcoming it with open arms are private industries, public buildings, and schools. It is estimated that it will also be adopted by retailers and banking systems in coming years to keep fraud in debit/credit card purchases and payment especially the ones that are online. This technology would fill in the loopholes of largely prevalent inadequate password system. In the long run, robots using facial recognition technology may also come to foray. They can be helpful in completing the tasks that are impractical or difficult for human beings to complete.
Sources
