Mini Project

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Team

Timeline

Process

What is Face Detection

Video

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Text/image I

How Face Detection Works

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Using C++

Face Detection

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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. It now plays an important role as the first step in many key applications -- including face tracking, face analysis and facial recognition.

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+INFO

Face detection -- also called facial detection -- is an artificial intelligence (AI) based computer technology used to find and identify human faces in digital images.

What is Face Detection?

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The methods used in face detection can be1. Knowledge-based2. Feature-based 3. Appearance-based.

Face detection applications use algorithms and ML to find human faces within larger images, which often incorporate other non-face objects such as landscapes, buildings and other human body parts like feet or hands. Face detection algorithms typically start by searching for human eyes -- one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, the mouth, nose, nostrils and the iris. Once the algorithm concludes that it has found a facial region, it applies additional tests to confirm that it has, in fact, detected a face.

How face detection works

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Appearance-based methods employ statistical analysis and machine learning to find the relevant characteristics of face images.

Appearancebased.

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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.

Featurebased

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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.

Knowledgebased

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1. 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.2. Easy to integrate. Face detection and facial recognition technology is easy to integrate, and most solutions are compatible with the majority of security software. 3. 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.

ADvantages of face detection

1.Massive data storage burden. The ML technology used in face detection requires powerful data storage that may not be available to all users. 2. 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. 3.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.

Disadvantages of face detection

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