Nav: Home

New face-aging technique could boost search for missing people

June 14, 2017

The method maps out the key features, such as the shape of the cheek, mouth and forehead, of a face at a certain age. This information is fed to a computer algorithm which then synthesises new features for the face to produce photographic quality images of the face at different ages.

A key feature of the method is that it teaches the machine how humans age by feeding the algorithm facial feature data from a large database of individuals at various ages. Consequently, the method improves on existing techniques, achieving greater level of accuracy.

The findings will be presented at the International Conference on Missing Children and Adults at Abertay University, Dundee in June, and have been published in the Journal of Forensic Sciences.

Professor Hassan Ugail, of Bradford's Centre for Visual Computing, is leading the research. He said: "Each year around 300,000 missing person cases are recorded in the UK alone. This has been part of our motivation in endeavouring to improve current techniques of searching for missing people, particularly those who have been missing for some considerable time."

The technique developed by the team uses a method of predictive modelling and applies it to age progression. The model is further strengthened by incorporating facial data from a large database of individuals at different ages thus teaching the machine how humans actually age. In order to test their results the researchers use a method called de-aging whereby they take an individual's picture and run their algorithm backwards to de-age that person to a younger age. The result is then compared with an actual photograph of the individual taken at the young age.

As a test case, the researchers chose to work on the case of Ben Needham. Ben disappeared on the Greek island of Kos on 24th July 1991, when he was only 21 months old. He has never been found, but several images have been produced by investigators showing how Ben might look at ages 11-14 years, 17-20 years, and 20-22 years. The team used their method to progress the image of Ben Needham to the ages of 6, 14 and 22 years. The resulting images show very different results, which the researchers believe more closely resemble what Ben might look like today.

An effective method needs to do two things: the synthesized images need to fit the intended age; and they need to retain the identity of the subject in age-progressed images. The results were evaluated using both machine and human methods, and in both, the images of Ben produced using this method were found to be more like the original picture of Ben than the images created as part of previous investigations.

Professor Ugail added: "No criticism is implied of existing age progression work. Instead we are presenting our work as a development and improvement that could make a contribution to this important area of police work. We are currently working with the relevant parties to further test our method. We are also developing further research plans in order to develop this method so it can be incorporated as a biometric feature, in face recognition systems, for example."

"Our method generates more individualised results and hence is more accurate for a given face. This is because we have used large datasets of faces from different ethnicities as well as gender in order to train our algorithm. Furthermore, our model can take data from an individual's relatives, if available, such as parents, grandparents and siblings. This enables us to generate more accurate and individualised ageing results. Current methods that exist use linear or one-dimensional methods whereas ours is non-linear, which means it is better suited for the individual in question."
-end-
Notes

'Facial Age Synthesis Using Sparse Partial Least Squares (The Case of Ben Needham)', by A.M. Bukar (MSc) and H. Ugail (Ph.D.), Centre for Visual Computing, University of Bradford, is published in the Journal of Forensic Sciences.

The third International Conference on Missing Children and Adults takes place from Wednesday 14 June to Friday 16 June 2017 at Abertay University, Dundee.

University of Bradford

Related Algorithm Articles:

Scientists use algorithm to peer through opaque brains
A new algorithm helps scientists record the activity of individual neurons within a volume of brain tissue.
Algorithm generates origami folding patterns for any shape
A new algorithm generates practical paper-folding patterns to produce any 3-D structure.
New algorithm tracks neurons in bendy brain of freely crawling worm
Scientists at Princeton University have developed a new algorithm to track neurons in the brain of the worm Caenorhabditis elegans while it crawls.
Does my algorithm work? There's no shortcut for community detection
Community detection is an important tool for scientists studying networks, but a new paper published in Science Advances calls into question the common practice of using metadata for ground truth validation.
'Cyclops' algorithm spots daily rhythms in cells
Humans, like virtually all other complex organisms on Earth, have adapted to their planet's 24-hour cycle of sunlight and darkness.
An algorithm that knows when you'll get bored with your favorite mobile game
Researchers from the Tokyo-based company Silicon Studio, led by Spanish data scientist África Periáñez, have developed a new algorithm that predicts when a user will leave a mobile game.
Algorithm identified Trump as 'not-married'
Scientists from Russia and Singapore created an algorithm that predicts user marital status with 86% precision using data from three social networks instead of one.
A novel positioning algorithm based on self-adaptive algorithm
Much attention has been paid to the Taylor series expansion (TSE) method these years, which has been extensively used for solving nonlinear equations for its good robustness and accuracy of positioning.
Algorithm can create a bridge between Clinton and Trump supporters
The article that received the best student-paper award in the Tenth International Conference on Web Search and Data Mining (WSDM 2017) builds algorithmic techniques to mitigate the rising polarization by connecting people with opposing views -- and evaluates them on Twitter.
Deep learning algorithm does as well as dermatologists in identifying skin cancer
In hopes of creating better access to medical care, Stanford researchers have trained an algorithm to diagnose skin cancer.

Related Algorithm Reading:

Best Science Podcasts 2019

We have hand picked the best science podcasts for 2019. Sit back and enjoy new science podcasts updated daily from your favorite science news services and scientists.
Now Playing: TED Radio Hour

Digital Manipulation
Technology has reshaped our lives in amazing ways. But at what cost? This hour, TED speakers reveal how what we see, read, believe — even how we vote — can be manipulated by the technology we use. Guests include journalist Carole Cadwalladr, consumer advocate Finn Myrstad, writer and marketing professor Scott Galloway, behavioral designer Nir Eyal, and computer graphics researcher Doug Roble.
Now Playing: Science for the People

#530 Why Aren't We Dead Yet?
We only notice our immune systems when they aren't working properly, or when they're under attack. How does our immune system understand what bits of us are us, and what bits are invading germs and viruses? How different are human immune systems from the immune systems of other creatures? And is the immune system so often the target of sketchy medical advice? Those questions and more, this week in our conversation with author Idan Ben-Barak about his book "Why Aren't We Dead Yet?: The Survivor’s Guide to the Immune System".