Face detection for beginners
Face detection is used everywhere nowadays, from unlocking phones to diagnosing diseases, we are using this advanced technology in our everyday life without realizing it. It has been growing in popularity in the last few years, more and more people are getting interested in this technology and would like to know more about it.
If you’re one of those people, whether your are familiar with it or not, then this article is for you.
Face detection refers to computer technology that is able to identify the presence of people’s faces within digital images. In order to work, face detection applications use machine learning and formulas known as algorithms to detecting human faces within larger images. These larger images might contain numerous objects that aren’t faces such as landscapes, buildings and other parts of humans (e.g. legs, shoulders and arms).
Face detection is a broader term than face recognition. Face detection just means that a system is able to identify that there is a human face present in an image or video. Face detection has several applications, only one of which is facial recognition, which means recognizing the who behind an image.
This technology can also be used to auto focus cameras. And it can be used to count how many people have entered a particular area. It can even be used for marketing purposes. For example, advertisements can be displayed the moment a face is recognized.
How face detection works
Face detection applications utilize calculations and ML to discover human faces inside bigger pictures, which frequently consolidate other non-face objects such as scenes, buildings and other human body parts like feet or hands. Face detection calculations ordinarily begin by looking for human eyes — one of the least demanding highlights to identify. The calculation might at that point endeavor to distinguish eyebrows, the mouth, nose, nostrils and the iris. Once the calculation concludes that it has found a facial locale, it applies extra tests to affirm that it has, in reality, recognized a face.
To assist ensure accuracy, the calculations ought to be prepared on huge information sets consolidating hundreds of thousands of positive and negative pictures. The preparing improves the algorithms’ capacity to decide whether there are faces in an picture and where they are.
The methods used in face detection can be knowledge-based, feature-based, template matching or appearance-based.
Face detection methods
Yan, Kriegman, and Ahuja displayed a classification for facial detection strategies. These strategies isolated into four categories, and the confront discovery calculations may have a place to two or more bunches. These categories are as follows :
The knowledge-based method depends on the set of rules, and it is based on human knowledge to detect the faces. Ex- A face must have a nose, eyes, and mouth within certain distances and positions with each other. The big problem with these methods is the difficulty in building an appropriate set of rules. There could be many false positive if the rules were too general or too detailed. This approach alone is insufficient and unable to find many faces in multiple images.
The feature-based method is to locate faces by extracting structural features of the face. It is first trained as a classifier and then used to differentiate between facial and non-facial regions. The idea is to overcome the limits of our instinctive knowledge of faces. This approach divided into several steps and even photos with many faces they report a success rate of 94%.
Template Matching method uses pre-defined or parameterised face templates to locate or detect the faces by the correlation between the templates and input images. Ex- a human face can be divided into eyes, face contour, nose, and mouth. Also, a face model can be built by edges just by using edge detection method. This approach is simple to implement, but it is inadequate for face detection. However, deformable templates have been proposed to deal with these problems.
The appearance-based method depends on a set of delegate training face images to find out face models. The appearance-based approach is better than other ways of performance. In general appearance-based method rely on techniques from statistical analysis and machine learning to find the relevant characteristics of face images. This method also used in feature extraction for face recognition.
Detecting faces in pictures can be complicated due to the variability of factors such as pose, expression, position and orientation, skin color and pixel values, the presence of glasses or facial hair, and differences in camera gain, lighting conditions and image resolution. Recent years have brought advances in face detection using deep learning, which presents the advantage of significantly outperforming traditional computer vision methods.
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.
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.
Use cases for face detection in augmented reality
AR and VR technologies have existed for decades, but only recently have they started gaining traction among consumers and businesses. Even those who may be completely out of touch with the latest trends must have heard about Pokémon Go and Snapchat.
Although AR and VR experiences may seem simple and intuitive for end users, they require plenty of well-orchestrated technologies in the background. One of such technologies is face tracking which serves as a foundation for many important functions. From determining head position and gaze direction to detecting facial expressions and creating 3D models — face tracking is a must.
Beauty and cosmetics :
A possibility to virtually try makeup, glasses, or jewelry is a must-have for brands in cosmetics, eye-wear, and lifestyle industries. In just a few clicks, customers can check out how the chosen product looks on them. Quality face tracking allows them to get the complete picture in real-time, even if they move their head around.
For customers, it’s a great way to easily find the product that suits them best, which is especially important in online shopping. Consequently, they are more likely to be satisfied with their purchase and remain loyal to their brand.
AR is also widely used in marketing. It helps create interactive campaigns that attract attention and encourage engagement.
An example of a successful global campaign is „Say Yes to the World” by Lufthansa. A part of the campaign was an AR installation called „Open Seats”. Seated on economy premium seats in front of a 7 square meter screen and captured by sensors and cameras, participants were invited to interact with a mix of animated 3D elements, actors and real-world environment in order to playfully discover interesting destinations.
Face filters are also frequently used to promote products and build loyalty. People can put on the colors of their sports team, take a selfie with a popular cartoon character or transform themselves into someone (or something) else.
Gaming and entertainment :
Canon created an app with creative photo editing options — from borders and stickers to 3D face filters such as animal masks. After editing their photos, users were able to connect their smartphone to Zoemini portable printer via Bluetooth and immediately print their photos, creating fun and long-lasting memories.
In the context of VR, face tracking technology is often used to achieve realistic virtual avatars whose facial expressions reflect those of the actual users.
Events and tours :
VR and AR can also enhance visitor experience at tours and events. A great example of such use case is Sakuya Lumina Night Walk in Osaka Castle, a tour that offers a stunning conception and production of immersive environment. Besides being amazed with surreal lighting and special effects, the visitors were also able to interact with lights and visualizations. Sakuya Lumina is a perfect example of blending high tech with historical heritage in order to create unforgettable experiences.
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