Artificial Face Perception
A great deal of effort has been put into developing software that can recognize human faces. Much of the work has been done by a branch of artificial intelligence known as computer vision which uses findings from the psychology of face perception to inform software design. Recent breakthroughs using noninvasive functional transcranial Doppler spectroscopy as demonstrated by Njemanze, 2007, to locate specific responses to facial stimuli have led to improved systems for facial recognition. The new system uses input responses called cortical long-term potentiation (CLTP) derived from Fourier analysis of mean blood flow velocity to trigger target face search from a computerized face database system. Such a system provides for brain-machine interface for facial recognition, and the method has been referred to as cognitive biometrics.
Another interesting application is the estimation of human age from face images. As an important hint for human communication, facial images contain lots of useful information including gender, expression, age, pose, etc. Unfortunately, compared with other cognition problems, age estimation from face images is still very challenging. This is mainly because the aging progress is influenced by not only personal gene but also many external factors. Physical condition, living style etc. may accelerate or slow aging process. Besides, since aging process is slow and with long duration, collecting sufficient data for training is a fairly strenuous work.
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