Editing Face perception (section)

==Artificial intelligence==
{{main|Facial recognition system}}
Much effort has gone into developing [[facial recognition system|software that can recognize human faces]].

This work has occurred in a branch of [[artificial intelligence]] known as [[computer vision]], which uses the psychology of face perception to inform software design. Recent breakthroughs use noninvasive functional [[transcranial Doppler]] spectroscopy to locate specific responses to facial stimuli.<ref name=":12" /> The new system uses input responses, called ''cortical long-term potentiation'', to trigger target face search from a computerized face database system.<ref name=":12">Njemanze, P.C. Transcranial doppler spectroscopy for assessment of brain cognitive functions. United States Patent Application No. 20040158155, 12 August 2004</ref><ref>Njemanze, P.C. Noninvasive transcranial doppler ultrasound face and object recognition testing system. United States Patent No. 6,773,400, 10 August 2004</ref> Such a system provides for brain-machine interface for facial recognition, referred to as ''cognitive [[biometrics]]''.

Another application is [[facial age estimation|estimating age from images of faces]]. Compared with other cognition problems, age estimation from facial images is challenging, mainly because the aging process is influenced by many external factors like physical condition and living style.The aging process is also slow, making sufficient data difficult to collect.<ref>{{Cite journal|url=http://pages.cs.wisc.edu/~huangyz/caip09_Long.pdf|title=Human age estimation by metric learning for regression problems|author=YangJing Long|journal=Proc. International Conference on Computer Analysis of Images and Patterns|year=2009|pages=74–82|url-status=dead|archive-url=https://web.archive.org/web/20100108055346/http://pages.cs.wisc.edu/~huangyz/caip09_Long.pdf|archive-date=8 January 2010 }}</ref>

===Nemrodov===
In 2016, Dan Nemrodov conducted multivariate analyses of EEG signals that might be involved in identity related information and applied pattern classification to [[event-related potential]] signals both in time and in space. The main target of the study were:

# evaluating whether previously known event-related potential components such as N170 and others are involved in individual face recognition or not
# locating temporal landmarks of individual level recognition from event-related potential signals
# figuring out the spatial profile of individual face recognition

For the experiment, conventional event-related potential analyses and pattern classification of event-related potential signals were conducted given preprocessed EEG signals.<ref>{{Cite journal|date=1 January 2019|title=Multimodal evidence on shape and surface information in individual face processing|url=https://www.sciencedirect.com/science/article/abs/pii/S1053811918319591|journal=NeuroImage|language=en|volume=184|pages=813–825|doi=10.1016/j.neuroimage.2018.09.083|issn=1053-8119|access-date=2 June 2021|archive-date=15 May 2021|archive-url=https://web.archive.org/web/20210515104335/https://www.sciencedirect.com/science/article/abs/pii/S1053811918319591|url-status=live|last1=Nemrodov|first1=Dan|last2=Behrmann|first2=Marlene|last3=Niemeier|first3=Matthias|last4=Drobotenko|first4=Natalia|last5=Nestor|first5=Adrian|pmid=30291975|s2cid=207211751}}</ref>

This and a further study showed the existence of a spatio-temporal profile of individual face recognition process and reconstruction of individual face images was possible by utilizing such profile and informative features that contribute to encoding of identity related information.