Face Recognition

Computers have been getting better and better at seeing movement on video. How is it that they read lips, follow a dancing girl or copy an actor making faces?

- from Andrew Blake. Introduction to Active Contours and Visual Dynamics. Visual Dynamics Group, Department of Engineering Science, University of Oxford

Image from Mitsubishi Electric Research Laboratories.

Vision

Contents

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Good Starting Places

AI in the News

How Google is teaching computers to see

Hartmut Neven, an engineering director leading visual-search initiatives for Google, predicts that near-perfection could come in the next decade. "Within 10 years, we can pretty much recognize, in principle, pretty much any object we're interested in," Neven said in a recent interview. "Scientific and technical progress is accelerating in an exponential (pace)." Neven began his research in 1992, and under his own forecasted timeline, is essentially more than halfway to meeting his goal. But the Google Goggles team is working on a system that canidentify faces in photos, as long as those people say it's OK for Google to include them in its database, Neven said.  ...

"Our ambition is nothing less than being able to recognize any object on the planet," Neven said. "But today, computer vision is not in that state yet. There are many things that, unfortunately, we cannot properly recognize."

Source

Apr 14 2011, By Milian, Mark
AI in the News

Computers That See You and Keep Watch Over You

... computer vision is moving into the mainstream. With this technological evolution, scientists predict, people will increasingly be surrounded by machines that can not only see but also reason about what they are seeing, in their own limited way. The uses, noted Frances Scott, an expert in surveillance technologies at the National Institute of Justice, the Justice Department’s research agency, could allow the authorities to spot a terrorist, identify a lost child or locate an Alzheimer’s patient who has wandered off.

Source

Jan 1 2011, By LOHR, STEVE

General Readings

AI in the News

Affective Programming Grows in Effort to Read Faces

 affective computing  aims to give computers the ability to read users’ emotions, or “affect.”

“Our digital world is for the most part devoid of rich ways of expressing our emotions,” said Rosalind Picard, director of the affective computing research group at the Massachusetts Institute of Technology Media Lab.

She has been working for more than two decades to translate emotions into 1’s and 0’s, the language of machines.

Source

Oct 15 2012, By WEINTRAUB, KAREN
affect
Publication

How Many Computers to Identify a Cat? 16,000

Google scientists created one of the largest neural networks for machine learning by connecting 16,000 computer processors, which they turned loose on the Internet to learn on its own.

Presented with 10 million digital images found in YouTube videos, what did Google’s brain do? What millions of humans do with YouTube: looked for cats.

The neural network taught itself to recognize cats, which is actually no frivolous activity.  ...

“It is worth noting that our network is still tiny compared to the human visual cortex, which is a million times larger in terms of the number of neurons and synapses,” the researchers wrote.

Despite being dwarfed by the immense scale of biological brains, the Google research provides new evidence that existing machine learning algorithms improve greatly as the machines are given access to large pools of data.

Source

Jun 25 2012, By Markoff, John
Google
AI in the News

Developing artificial intelligence systems that can interpret images

Today, Torralba is a tenured associate professor of electrical engineering and computer science at MIT, and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he develops AI systems that can interpret images to understand what scenes and objects they contain. I wanted to build systems that could put objects into context, to try to understand how different objects relate to each other, he says. Torralba is also attempting to develop systems that can scan a short video clip and predict what is likely to happen next, based on what people or objects are in the scene. To do this, the systems will need to understand what actions each object or person in the scene is capable of making, and what their limitations are.

Source

Dec 12 2011
intention
AI in the News

Facial recognition software spots family resemblance

FACIAL recognition software that's as good as people at spotting family resemblances could help to reunite lost family members - or help the likes of Facebook work out which of your friends are blood relatives. To do this, the team used a database of public figures and their parents or children - such as French president Nicolas Sarkozy and his son Jean - and fed the program 320 pairs each of parent-child matches and mismatches. The software then compared the difference between a test pair of photos with pairs of photos in its database. In tests using 160 pairs - 80 parent-child matches and 80 mismatches - the system had a success rate of 68 per cent.

Source

Dec 7 2011
Link

Emotionally Intelligent Interfaces

From the Rainbow Research Group at the University of Cambridge Computer Laboratory. "Facial displays are an important channel for the expression of emotions and are often thought of as projections or 'read out' of a persons mental state. Our research objective is to develop emotionally intelligent interfaces that make use of this readily available input modality to adapt and respond to its user."

Source

2011
AI in the News

New Insight Into How Bees See Could Improve Artificial Intelligence Systems

New research from Monash University bee researcher Adrian Dyer could lead to improved artificial intelligence systems and computer programs for facial recognition. ... Dr Dyer said the research could be applied in the areas of new technology, particularly the development of imaging systems. "What we have shown is that the bee brain, which contains less than 1 million neurons, is actually very good at learning to master complex tasks. Computer and imaging technology programmers who are working on solving complex visual recognition tasks using minimal hardware resources will find this research useful," Dr Dyer said. "Most current artificial intelligence (AI) recognition systems perform poorly at reliably recognising faces from different viewpoints. However the bees have shown they can recognise novel views of rotated faces using a mechanism of interpolating or image averaging previously learnt views."

Source

Jan 26 2009
bees

Hardware and Software

Link

OpenCV

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify the code.

The library has more than 2500 optimized algorithms, which includes a comprehensive set of both classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used to detect and recognize faces, identify objects, classify human actions in videos, track camera movements, track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch images together to produce a high resolution image of an entire scene, find similar images from an image database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and establish markers to overlay it with augmented reality, etc. OpenCV has more than 47 thousand people of user community and estimated number of downloads exceeding 5 million. The library is used extensively in companies, research groups and by governmental bodies.

Source

News

AI in the News

Privacy Visor Confuses Face Recognition Camera

"Hiding behind your glasses" just took on a whole new meaning.

Source

Jan 23 2013
AI in the News

Affective Programming Grows in Effort to Read Faces

 affective computing  aims to give computers the ability to read users’ emotions, or “affect.”

“Our digital world is for the most part devoid of rich ways of expressing our emotions,” said Rosalind Picard, director of the affective computing research group at the Massachusetts Institute of Technology Media Lab.

She has been working for more than two decades to translate emotions into 1’s and 0’s, the language of machines.

Source

Oct 15 2012, By WEINTRAUB, KAREN
affect
AI in the News

Artificial intelligence: Getting better at the age guessing game

The active learning algorithm is faster and more accurate in guessing the age of an individual than conventional algorithms. They have, for example, developed computer algorithms for facial age classification -- the automated assignment of individuals to predefined age groups based on their facial features as seen on video captures or still images. A person can teach a computer to make better guesses by running its algorithm through a large database of facial images of which the age is known using sets of labeled images, but acquiring such a database can be both time-consuming and expensive. The technology could find use, for example, in digital signage where the machine determines the age group of the viewer and displays targeted advertisements designed for those age groups, or in interactive games where the machine automatically presents different games based on the players' age range.

Source

Feb 1 2012
AI in the News

Developing artificial intelligence systems that can interpret images

Today, Torralba is a tenured associate professor of electrical engineering and computer science at MIT, and an affiliate of the Computer Science and Artificial Intelligence Laboratory (CSAIL), where he develops AI systems that can interpret images to understand what scenes and objects they contain. I wanted to build systems that could put objects into context, to try to understand how different objects relate to each other, he says. Torralba is also attempting to develop systems that can scan a short video clip and predict what is likely to happen next, based on what people or objects are in the scene. To do this, the systems will need to understand what actions each object or person in the scene is capable of making, and what their limitations are.

Source

Dec 12 2011
intention
AI in the News

Facial recognition software spots family resemblance

FACIAL recognition software that's as good as people at spotting family resemblances could help to reunite lost family members - or help the likes of Facebook work out which of your friends are blood relatives. To do this, the team used a database of public figures and their parents or children - such as French president Nicolas Sarkozy and his son Jean - and fed the program 320 pairs each of parent-child matches and mismatches. The software then compared the difference between a test pair of photos with pairs of photos in its database. In tests using 160 pairs - 80 parent-child matches and 80 mismatches - the system had a success rate of 68 per cent.

Source

Dec 7 2011
AI in the News

How Google is teaching computers to see

Hartmut Neven, an engineering director leading visual-search initiatives for Google, predicts that near-perfection could come in the next decade. "Within 10 years, we can pretty much recognize, in principle, pretty much any object we're interested in," Neven said in a recent interview. "Scientific and technical progress is accelerating in an exponential (pace)." Neven began his research in 1992, and under his own forecasted timeline, is essentially more than halfway to meeting his goal. But the Google Goggles team is working on a system that canidentify faces in photos, as long as those people say it's OK for Google to include them in its database, Neven said.  ...

"Our ambition is nothing less than being able to recognize any object on the planet," Neven said. "But today, computer vision is not in that state yet. There are many things that, unfortunately, we cannot properly recognize."

Source

Apr 14 2011, By Milian, Mark

Classic Articles & Books

Publication

Edge detection in pictures by computer using planning

Source

By Kelly, M., 1971
classic
Publication

Visual identification of people by computer

Source

By Kelly, M.D., 1970
classic
Publication

A Man-Machine Facial Recognition System: Some Preliminary Results

Source

By Bledsoe, Woodrow W. and Helen Chan , 1965
classic

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