A field of artificial intelligence (AI), Computer Vision (CV) 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. In other words, computer vision enables computer systems to see, observe and understand.
At an abstract level, the goal of computer vision problems is to use the observed image data to infer something about the world. Et.el (Computer Vision: Models, Learning, and Inference, 2012)
Computer vision is used in industries ranging from Health Case, Energy, Defence, and utilities to manufacturing and automotive – and the market is continuing to grow.
With evolution of mobile devices, we are seeing steep growth in the number of photos taken by each smartphone user. A very few endsup being on the social networking platforms. Others remain hidden in the mobile memory.
To get the most out of image data, we need computers to “see” an image and understand the content.
1. A person can describe the content of a photograph they have seen once.
2. A person can summarize a video that they have only seen once.
3. A person can recognize a face that they have only seen once before.
1. Object Classification: What broad category of object is in this photograph?
2. Object Identification: Which type of a given object is in this photograph?
3. Object Verification: Is the object in the photograph?
4. Object Detection: Where are the objects in the photograph?
5. Object Landmark Detection: What are the key points for the object in the photograph?
6. Object Segmentation: What pixels belong to the object in the image?
7. Object Recognition: What objects are in this photograph and where are they?
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.
One of the most important applications of face detection, however, is facial recognition. Face recognition describes a biometric technology that goes way beyond recognizing when a human face is present. It actually attempts to establish whose face it is. The process works using 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. While facial recognition isn’t 100% accurate, it can very accurately determine when there is a strong chance that an person’s face matches someone in the database.
There are lots of applications of face recognition. Face recognition is already being used to unlock phones and specific applications. Face recognition is also used for biometric surveillance. Banks, retail stores, stadiums, airports and other facilities use facial recognition to reduce crime and prevent violence.
So in short, while all facial recognition systems use face detection, not all face detection systems have a facial recognition component.
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