Facial recognition technologies are complex and error rates remain significant depending on the imaging process and subject. As deployment and user numbers increase, these errors will become more prevalent without significant modernization of capture procedures.
IEC and ISO work together to develop international standards for ICT through their joint technical committee (ISO/IEC JTC 1). Subcommittee 37 covers biometrics and has begun work on the new ISO/IEC 24358 standard.
e-tech spoke to Patrick J. Grother, who leads the work of SC 37, to find out more about the new standard.
Facial recognition is a process. It starts with taking a photograph of a face. Then a face recognition algorithm, nowadays built with artificial intelligence (AI) technologies, is used to extract identity-related features from the image. These features can then be matched against features previously extracted from other images. These might reside in a database, for example.
Facial recognition is being used in an ever-increasing array of applications. The main ones are in passport and driving license issuance, but it is also used for building access and border control, and in law enforcement investigations.
Face recognition systems occasionally make mistakes. They can fail to match a known user – a false negative - or they can incorrectly associate different users – a false positive. These outcomes depend on the properties of the input photographs.
In particular, an image can be degraded by image quality aspects such as poor exposure or blur, or by aspects of how the subject presents to the camera e.g. by looking down, or by making an unusual facial expression. These possibilities motivate the new ISO/IEC 24358 standard. It aims to minimize facial recognition errors by defining a new generation of cameras that understand the image they’re trying to collect. The current situation is that often generic “dumb” cameras are used that naively accept poorly presented images.
So this standard conceives of face-aware cameras tightly coupled to image quality assessment measurements made in real-time. In so doing, it aims to bring to face recognition at least the maturity that characterizes fingerprint and iris acquisition. Those modalities benefited from the outset from the use of devices that are aware of the characteristic they’re trying to acquire – friction ridges and circular structures in the eye respectively. Face recognition has only recently begun to see use of face-aware cameras, particularly in e-Passport gates and mobile phones.
This standard is about making a new generation of smart cameras, technically better cameras. A big part of that, for multiple reasons, is to acquire images at higher resolution. We know that a lot of cell phones can take very high-resolution photos and lots of cameras have very high resolution, more than you usually need and that information turns out to be useful for multiple reasons. By requiring collection of higher resolution images, the new standard aims to allow face recognition algorithms to access more fine-grained information in faces. This information supports accurate facial recognition of twins (contemporary systems won’t distinguish between identical twins), improved human adjudication of photos for example to support courtroom testimony, and also better detection of “attack” images (e.g. from spoofing attempts).
A growing number of civil identity management and law enforcement applications are using vast numbers of face images, which could later serve as references. There are also new programmes using facial recognition, such as the European Union for biometric exit confirmation. The United States is piloting face for exit in airports, while in India, the Aadhaar programme has started allowing face recognition for authentication.
Some technical issues include:
Humans involved in the facial recognition process make mistakes, especially when image quality is poor:
As mentioned earlier, this standard specifies properties of next-generation biometric face capture subsystems intended to improve the suitability of photographs for automated face recognition, reduce the variability in those photographs, improve support for human face identification, and impede tampering and illicit modification of photographs.
It also includes specifications for new functionalities for face image capture subsystems that target the quality of images. Its primary role is in collection of pristine face images from cooperating subjects that are suited to reside in an authoritative enrolment database. Additionally, it addresses other issues, for example, it adds support for forensic human adjudication; it formalizes compression; includes protection against image manipulation and tampering; merges printing processes.
Find out more about the work of SC 37.