Determining familial matches with Facial Recognition

Photo courtesy of UCF
Photo courtesy of UCF

Last month, researchers at the University of Central Florida presented a new facial recognition tool at the IEEE Computer Vision and Pattern Recognition conference in Columbus, Ohio. 

While there is no shortage of facial recognition tools used by companies and governments the world over, this one is unique in that its aim is to unite or reunite children with their biological parents.

The university’s Center for Research in Computer Vision initially got to work by creating a database of more than 10,000 images of famous people–such as politicians and celebrities–and their children.

It works by using a specially designed algorithm that breaks the face down into sections, and using various facial parts as comparisons; they are then sorted according to which matches are the most likely.

Though software for this purpose already exists, this tool was anywhere from 3 to 10 percent better than those programs, and it naturally surpasses the recognition capabilities of humans, who base their decisions on appearance rather than the actual science of it. It also reaffirmed the fact that sons resemble their fathers more than their mothers, and daughters resemble their mothers more than their fathers.

What other ways could this tool be useful?

Computer Vision aids flow cytometry

Photo courtesy of the USCD Jacob School of Engineering
Photo courtesy of the USCD Jacob School of Engineering

Engineers at the University of California, San Diego, are using Computer Vision as a means of sorting cells, and thus far have been able to do so at a rate of 38 times faster than before. This process of counting and sorting cells is known as  flow cytometry.

The analysis of the cells helps to categorize them based on their size, shape, and structure, and also can distinguish if they are benign or malignant, information that could be useful for clinical studies and stem cell characterization.

While this type of research was occurring before, it’s a job that has traditionally taken a lot of time. But now, the use of a camera on a microscope can analyze information faster–cutting the time from between 0.4 and 10 seconds to observe and analyze a single frame down to between 11.94 and 151.7 milliseconds.

In what ways do you see this technology making advancements in the medical and clinical world? How else can you imagine it benefitting science?

Counting grapes with Computer Vision

Photo courtesy of Carnegie Mellon
Photo courtesy of Carnegie Mellon
It’s not secret that Computer Vision is an asset in the agricultural world, yet it’s still interesting to discover the new ways in which it is being put to you. For example, researchers at Carnegie Mellon University’s Robotics Institute published a study demonstrating how visual counting – one of the elementary Computer Vision concepts – is a way of estimating the yield of a crop of grapes.

Using an HD camera, a special lighting system, and a laser scanner, the setup can count grapes as small as 4mm in diameter, and using algorithms, is able to use the number of grapes and convert that to an estimated harvest yield. And while the margin of error is 9.8 percent, in humans, it’s 30, demonstrating that the Computer Vision system is more efficient and possibly more cost-effective.

Computer Vision studies bird flocking behavior

Photo courtesy of Andreas Trepte.
Photo courtesy of Andreas Trepte.
Flocking is a behavior exhibited in birds, which is similar to how land animals join together in herds. And while there is an intricate pattern to this flocking, it’s difficult to establish exactly how birds communicate to keep this form. Their movements are synchronous, but the question is: how do birds on the outer edges of the flock stay in sync and help guide the group? Luckily, we have computer vision to help answer that question.

Before, scientists used to simulate this behavior and then compare it to what occurs with birds in real life in an attempt to demonstrate the how and the why. However, now computer vision can measure both position and velocity of objects in a frame, thanks to the work of William Bialek at Princeton University, which is demonstrating that birds are capable of matching the speed and direction of their neighbor birds.

Additionally, the concept of “critical point” helps explain this, showing that the social desires of the birds overwhelms the motivation of each individual bird, as they work toward flying as a collective flock and not as solo birds.

There still remains more to be seen and explored, but check out this study for further reading.

Computer Vision aids endangered species conservation efforts

Photo by Dr. Paddy Ryan/The National Heritage Collection
Photo by Dr. Paddy Ryan/The National Heritage Collection

In an effort to help protect and conserve endangered species, scientists have been tracking and tagging them for years. However, there are some species that are either too large in population or too sensitive to tagging, and researchers have been working on another way to track them.

Now, thanks to SLOOP, a new computer vision software program from MIT, identifying animals has never been easier. A human sorting through 10,000 images would likely take years to properly identify animals, but this computer program cuts down the manpower and does things much quicker. Through the use of pattern-recognition algorithms, the program is able to match up stripes and spots on an animal and return 20 images that are likely matches, giving researchers a much smaller and more accurate pool to work with. Then the researchers turn to crowdsourcing, and with the aid of adept pattern-matchers, are able to narrow things down even more, resulting in 97% accuracy. This will allow researchers to spend more practical time in the field working on conversation efforts instead of wasting time in front of a computer screen.

Computer Vision sees faces in the clouds

Image courtesy of Shinseungback Kimyonghun
Image courtesy of Shinseungback Kimyonghun

Computer Vision has many practical uses, ranging from security enhancement to making our lives easier, but what about art?

A new project, Shinseungback Kimyounghung, was launched by two South Koreans who are using Computer Vision to find faces in the clouds. This is similar to how children often lay on their backs and point out shapes in the sky, but instead, relies on computer algorithms to spot faces.

However, while the project appears artistic on surface level, examining it deeper reveals a study comparison how computers see versus how humans see. What the end result will be isn’t yet clear at this point, but it’s an interesting and thoughtful take on the subject nonetheless.

Robots discovering objects through Computer Vision

herbrobotWhile having a personal robot ala Judy in the television show, the Jetson’s, may have been the dream of every child, now such a thing is closer to becoming a reality.

Meet HERB, a Home-Exploring Robot Butler created by researchers at Carnegie Mellon University, as part of the Lifelong Robotic Object Discovery.

The robot, which is armed with a Kinect camera and relies on computer vision, has programming that includes a memory loaded with digital models and images of objects to aid in recognition. The goal is to create a robot that not only recognizes what it has already been taught, but grows that information on its own, without the database being expanded manually. It does this not only through simply seeing things, but also by exploring the environment and interacting with objects in it.

The Kinect camera helps to aid HERB in three-dimensional recognition, while the location of an object is also telling. Additionally, HERB can distinguish between items that move and those that don’t. As it interacts with its environment, it eventually is able to determine if something is an object, meaning if something can be lifted.

This information can later lead to robots that do things for humans, such as bringing them items or helping to clean. And while there is still a ways to go, the possibilities seem to be endless.

Makeup can mask facial recognition

004795_10_fig2As advancements in facial recognition are made, many people have become increasingly worried about protecting or maintaining their privacy. And while there are ways to hide or obscure a face, it has been thought by many that makeup wasn’t enough to fool that cameras.

However, researchers in Michigan and West Virginia have set out to disprove such an idea, demonstrating how makeup actually can change the appearance of an individual. While the way someone’s head is held, the expressions he or she may make, and the lighting don’t confuse computers, things such as natural aging or face-altering methods like plastic surgery can. Now, makeup can be added to the list.

This is because makeup can change the shape and texture of a face, by playing natural contours of the face up or down, changing the appearance of the quality and size of certain features, and even camouflaging identifying marks, including scars, birth marks, moles, or tattoos. Of course not a simple application of makeup is enough to do the rick, but heavy layers of makeup can be.

To find out more about this study and its aims, refer to an article on the subject that describes it in further detail.

Image recognition used with Instagram, other social media sites

starbucksInstagram users threw a fit late last year when the popular photo app announced its new terms of services, many which users felt were a violation of privacy.

The main thing users took issue with was the ownership of photos, that is, if Instagram is allowed to take photos from its users and re-appropriate them as the company sees fit.

But what many people don’t realize is that their photos are already being used in the marketing and advertising worlds. Just consider gazeMetrix, a startup that uses computer vision and machine learning when sorting through photos on social media platforms, in order to recognize brand logos and trademarks being photographed.

In finding the use and appearance of these logos, companies are then able to promote their brands more effectively by targeting ads to the proper markets, see how these items are being used, and communicate to users of their specific products.

An article on Forbes.com provides examples of the many ways this data can be used. What are some other potential uses?