Brains in Fashion
What The #10YearChallenge Could Do
19th February 2019
By now you’ve probably heard of ‘The 10 Year Challenge’ that has gained traction across social media platforms, in particular, Facebook and Instagram. Maybe you participated in the excitement, I definitely did.
Photo credit: Kristy Winter
The challenge is to post a photograph from 10 years ago next to a photo of yourself this year to show how you’ve aged visually. This has typically been shared across both Facebook and Instagram (both owned by Facebook).
Now there can be huge issues with sharing information online, especially photos. Facebook (in its terms and services) has a “non-exclusive, transferable, sub-licensable, royalty-free, worldwide license” to the content and information you post on Facebook – including your photos. So let’s translate that into English:
“Non-exclusive” means that you can share the information you posted to Facebook with whoever you want. The information you share with Facebook isn’t a secret and can be shared again on other platforms.
“Transferable” and “sub-license” refers to Facebook’s ability to ‘give’ the licence to your information to another company or person, without your permission. This is similar to when you send a selfie to a friend, they could then choose to send that on to another person.
“Royalty-free worldwide license” means that Facebook can use your information or photos however they want, wherever they want without asking your permission or paying you.
With this in mind, it is no wonder why the internet started to panic in response to a tweet made by Kate O’Neill (and her WIRED article):
Me 10 years ago: probably would have played along with the profile picture aging meme going around on Facebook and Instagram
Me now: ponders how all this data could be mined to train facial recognition algorithms on age progression and age recognition
@kateo. 13 Jan 2019.
It is possible that Facebook could be using this information, without your permission, to build a dataset for potential facial recognition software. But that does not mean it is necessarily bad. Facial recognition software, such as age-progression, is incredibly useful.
An example of its use is estimating the visual appearance of missing persons who have been missing for a substantial amount of time, in particular children. Estimating the visual appearance of missing people years after their disappearance helps to keep the search for them alive. Roughly 900 children have be found due to age-progression software.
In the case of Joseph Carson, he went missing at the age of 3 (left image) in Phoenix. An age-progression composite of him was developed and broadcasted (middle image). Someone recognized him and he was returned to his family at the age of 9 (right image).
The three images of Joseph Carson in relation to his missing person case
New software specific to facial recognition and estimating age-progression is constantly in development. There are challenges with this: biological age can be affected by a range of factors including chronic illnesses, potentially affecting the visual appearance of age, and there is a lack of datasets available for facial ageing. Software such as the work coming out of the University of Bradford can produce synthesised images of missing persons based on past images of the individuals. However, improvements with age-progression software can always be made.
By incorporating the images from the #10yearchallenge into analysis for improved age-progression software, maybe more missing persons can be identified and returned to their families in the future.
Bukar, Ali M. and Ugail, Hassan. 2017. “Facial age synthesis using sparse partialleast squares (The case of Ben Needham).” Journal of Forensic Sciences 62 (5): 1205-1212
Facebook. 2019. “Terms of Service.” Accessed January 22, 2019. https://www.facebook.com/terms
Goldman, Russell. 2009. “Jaycee Dugard looks like the image Forensic Artists created to help find her.” Accessed Janurary 23, 2019. https://abcnews.go.com/Technology/AheadoftheCurve/hundreds-missing-children-found-age-progression-images/story?id=8830185
Jylhävä, Juulia., Pedersen, Nancy. L. and Hägg, Sara. 2017. “Biological Age Predictors.” EBioMedicine 21: 29-36.
Lampinen, James., Arnal, Jack D., Adams, Jennifer., Courtney, Kady. and Hicks, Jason L. 2012. “Forensic age progression and the search for missing children.” Psychology, Crime & Law 18 (4): 405-415
O’Neill, Kate. 2019. “Facebook's '10 year challenge' is just a harmless meme—right?” Accessed January 21, 2019. https://www.wired.com/story/facebook-10-year-meme-challenge/
Zhu, Zijiang., Chen, Hang., Hu, Yi. and Li, Junshan. 2018. “Age estimation algorithm of facial images based on multi-label sorting.” EURASIP Journal on Image and Video Processing 2018 (114): 1-10