Introducing Facial Recognition
This is one of those typical scenes in movies that Facial Recognition (FR) is put into action. For those using Facebook, you will also recall that Facebook automatically tagged the name of faces in the image that you upload. In physical world applications, FR is being used to verify an individual's identity in Immigration and making Payment. When we put all these together, FR is doing the job of Identity Management. FR is one of the many tools available that can perform this task.
FR is becoming a very common topic in the last few years, mainly due to the wide application in China for Law Enforcement, Payment, Customer Service, etc. There are both positive & negative outcomes that resulted from the usage of facial recognition as well as strength and weakness. In many instances, the weaknesses are not being mentioned and this is one of the challenges in giving the wrong expectation for real-life implementation.
Chinese man caught by facial recognition at pop concert
https://www.bbc.com/news/world-asia-china-43751276
UK police use of facial recognition technology a failure, says report
https://www.theguardian.com/uk-news/2018/may/15/uk-police-use-of-facial-recognition-technology-failure
China’s ‘Big Brother’ surveillance technology isn’t nearly as all-seeing as the government wants you to think
https://www.businessinsider.sg/china-facial-recognition-limitations-2018-7/?r=US&IR=T
Eyes set on the wanted in Penang
https://www.thestar.com.my/news/nation/2019/01/03/eyes-set-on-the-wanted-in-penang-countrys-first-facial-recognition-camera-system-to-detect-and-track/
Lawmakers can't ignore Facial Recognition's Bias anymore
https://www.wired.com/story/amazon-facial-recognition-congress-bias-law-enforcement/
While technology can achieve relatively high accuracy now as compared to a few years back, the outcome of real application remains mixed. What I feel is the lack of a holistic approach in deployment of FR where the focus is solely in the technological aspect.
While the accuracy is high, what is the speed in matching and the hardware required to achieve this? Can I support my operation with this speed of return?
Does the placement of the cameras enable capturing of people's face or it is mainly seeing people looking away from the camera's view?
How's the environment going to interact to assist or limit the probability of getting faces?
How does Light Reflecting Markup affects the performance of FR? How about those Anti-FR approaches to evade detection?
Applied FR on the field is both an Art and Science where one needs to take into consideration various factors that constitute a good deployment. One need to consider things such as Infrastructural Cost, Hardware Cost, Customer's SOP, Human Behaviour, Response Time, Environmental Effect, etc. Putting all these together to achieve optimal performance given the situation on the ground.
Just as each individual are a unique person, each deployment needs to be treated differently and no single deployment is exactly the same. Some required major rework on the approach while some just need minor tweaks to adapt to the situation. All these required an extensive amount of experiences and time on the ground to conduct studies before an appropriate solution can be proposed. Even then, the implementation should be the best approach using Design Thinking and cater for adaptations and tuning along the way.
Regardless of your requirement, the considerations of having an optimised facial recognition solution remain largely similar. This article series on Facial Recognition aims to share with you what we have seen over the last 10 years on the field. Apart from technology and it's relevancy to each situation, we will consider some of the fundamentals that affect what you will be getting eventually.
Thank you for reading and stay tuned for the next article and do vote for me if you like this article.
