Article: Davis, J. P., Jansari, A., & Lander, K. (2013). ‘I never forget a face!’. The Psychologist, 26(10), 726-729.
Summary:
The authors summarize the existing literature about super-recognisers, or those individuals scoring high on face-recognition tests, into three broad areas: general information, police officers, and the general population. In the first section – general information – the authors provide information about super-recognisers and prosopagnosia. Prosopagnosia, or face blindness, is when someone loses their ability to recognize familiar faces (including their own). This disorder can either stem from brain damage or genetic inheritance, and about two percent of the population has it.
At the other end of the face recognition spectrum – or Gaussian distribution – are those that are superior at recognizing faces, with an uncanny ability for facial recognition. Davis and colleagues comment that the belief that the super-recognisers’ ability is limited to face recognitions indicates support for the face recognition spectrum (prosopagnosia at one end and super recognizers at the other). They continue to note that facial recognition by humans can be superior to machines because faces are dynamic which can trip up machines.
In the next section, Davis and colleagues focus more closely on the police and super-recognisers. The bulk of the research in this section stems from the Annual Conference of the British Psychology Society presentation “Facial identification from CCTV: Investigating predictors of exceptional face recognition performance amongst police officers” (2013) by Davis, Lander, & Evans. These authors asked police officers who were deemed super-recognisers various questions and tested them on their recall ability. Some of the findings indicate that officers’ families do not share the similar super-recogniser skills and that these officers based their identifications on distinctive facial features. In addition, the officers that provided broad strategies for facial recognition did worse than those with more narrower strategies. Lastly, the super-recogniser officers did well on celebrity recognition tests.
In the section on the general population, the authors pull from an unpublished study by Ashok Jansari that deployed the Cambridge Face Memory Test at London’s Science Museum. Their preliminary findings indicate that face recognition falls within a Gaussian distribution and fewer than 10 people had super recognition scoring. Davis and colleagues then presents individual differences that influences facial recognition tasks, such as introvert/extrovert personality, as well as different processing difficulties for some (holistic processing/ inverted faces/ whole-part effect).
Reflection:
I recommend taking the Cambridge Face Memory Test to see where you fall on the face recognition distribution. This is the same task that was given to the officers and general population to take in some of the mentioned studies.
Interestingly, since the article was published, the Metropolitan Police actually formed a team of super-recognisers. This team complements the millions of CCTVs scattered across London. Although these recognizers are human, it adds another dimension of surveillance to the streets of London, something some are trying to pull back on.
The authors did mention the uniqueness of humans versus technology in facial recognition. In particular, humans might be better than machines at identifying dynamic changes in human faces. However, with increasing advancements in technologies (and specifically biometrics), I wonder, will more advanced AI facial recognition software make this new unit obsolete in the next ten years or so? Or, do humans have some unique ability to notice dynamic changes that machines will not be able to mimic?
Lastly, it was interesting that, in the presented research by Davis, Lander, & Evans (2013), mostly white male officers thought they identified more black than white offenders. The authors attempted to attribute this finding to extensive contact with certain minority groups based on their policing jurisdiction. However, it makes me wonder the role that racial discrimination plays in their identifications and if some in the super-recognizer units simply reinforce policing discrimination? I am curious on their verification methods. How do they ensure relatively accurate recognitions as well as to eliminate any potential for bias and discrimination?
Questions:
- How does facial recognition surveillance by humans differ from facial recognition surveillance by machines?
- And, is there one that is preferable?
- Is one better at deterrence (both general and specific)?
- What are the privacy/ethical implications with humans vs. machines?
- Is there potential for reinforcing discriminatory policing practices with super-recogniser policing?
- What verification methods would need to be put into place?
- How can the general populations’ super-recogniser skills play into crowd-sourcing?
