The computer vision field is rather extensive. It has applications from industry to homes. However, many of the underlying processes and techniques are the same for all application areas. Next, an overview of these processes and techniques is given. Digital image acquisition and processing are the first topics since they form the basis for higher level processing, such as pattern recognition, when computer vision is considered. Machine learning and pattern recognition are the second two topics, since they are also applied extensively in computer vision. In fact, many machine learning and pattern recognition techniques, such as neural networks and support vector machines (SVM) are also used in many other fields than computer vision.
Visual and Facial Recognition Technology
Throughout history, humans have developed machines to do work for us. More recently, this has included machines that imitate our senses, like our vision. Vision recognition technologies are technologies that can see and label things. These technologies let machines, robots, and apps see and understand the world as we see it.
Computer vision (CV) is a type of computer engineering. It involves teaching computers to “see” digital images such as photos and videos. Engineers who work in this field have a variety of tasks. One thing they do is to find ways to use digital cameras with devices and computers. They also find ways to teach computers to recognize images and videos. This is done through coding or machine learning.
There are different types of computer vision. Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.
Computer vision works much the same as human vision, except humans have a head start. Human sight has the advantage of lifetimes of context to train how to tell objects apart, how far away they are, whether they are moving and whether there is something wrong in an image.
Computer vision trains machines to perform these functions, but it has to do it in much less time with cameras, data and algorithms rather than retinas, optic nerves and a visual cortex. Because a system trained to inspect products or watch a production asset can analyze thousands of products or processes a minute, noticing imperceptible defects or issues, it can quickly surpass human capabilities.
Computer vision is used in industries ranging from energy and utilities to manufacturing and automotive – and the market is continuing to grow. The computer may look for text, images or faces.
Here are a few examples of established computer vision tasks:
• Image classification sees an image and can classify it (a dog, an apple, a person’s face). More precisely, it is able to accurately predict that a given image belongs to a certain class. For example, a social media company might want to use it to automatically identify and segregate objectionable images uploaded by users.
• Object detection can use image classification to identify a certain class of image and then detect and tabulate their appearance in an image or video. Examples include detecting damages on an assembly line or identifying machinery that requires maintenance.
• Object tracking follows or tracks an object once it is detected. This task is often executed with images captured in sequence or real-time video feeds. Autonomous vehicles, for example, need to not only classify and detect objects such as pedestrians, other cars and road infrastructure, they need to track them in motion to avoid collisions and obey traffic laws.(7)
• Content-based image retrieval uses computer vision to browse, search and retrieve images from large data stores, based on the content of the images rather than metadata tags associated with them. This task can incorporate automatic image annotation that replaces manual image tagging. These tasks can be used for digital asset management systems and can increase the accuracy of search and retrieval.
Facial Recognition Technologies
In the current era, a human face is not only used for social interactions but also extensively utilized to unlock various devices. This particular technology is well known as Face recognition. It utilizes the human face to act as a password for multiple devices employing biometric face recognition technology. A person is no longer required to remember passwords that can be easily forgotten, lost or stolen. As technology advances, facial recognition system is getting more potential in various industries ranging from Enterprises, Hospitals, Law enforcement, Retail, Hospitality to Marketing.
Facial Recognition Technology (FRT) is a technology that identifies human faces. The process they use is like the way humans recognize each other. A computer’s facial recognition system is like your facial recognition system. You see someone’s face with your eyes. A smartphone takes an image of someone’s face with its camera. Your brain takes the features of the face and stores it in your memory. This is what lets you remember people later. A computer does the same using algorithms.
Faces are unique. Like a fingerprint, we can measure and compare them. The term for measuring biological features is biometrics. Facial biometric software measures and maps parts of a face. This includes things like the shape and color of eyes, noses, mouths and chins. We call these measurements nodal points. A geometric map of a person’s face needs about 80 nodal points.
Face scanner is the future generation recognition system that provides an incredibly versatile human verification process. Its application is not restricted for the security concerns only but also promptly expands the outreach in a commercial domain.
Facial recognition system captures still images or live video images to identify an individual through a certain distance as it does not involve any physical interaction with the person. The images captured will be in the form of nodal points that is compared with the existing nodal points present in the system to identify a pre-registered individual.