Will computers (man-made perception systems) be ever as
recognizing what they see as are humans?
This was the question which drove us to look more closely into the
way perception is done in biological
systems. It was realized then that not only the results
from Computer Vision and Pattern Recognition, commonly used by
working in the area, should be used for this problem, but also
obtained in Neurobiology.
We are still far from modeling the entire brain (or
visual cortex), which will unlikely ever happen, but some advances
have been already made, most important of which are
- Setting a distributed, non Von-Neumann, neural-network
based paradigm for video processing and memorization, which allows
one to incorporate the visual attention and other visual perception
mechanisms performed in the human brain , and
- Establishing the framework for testing the performance of
the thus developed "mini models of brains" - the way we call our
Facial Video Memory system.
One of the major advantages of the first result is that it
allows one to deal with the continuous flow of video images, as
they are in video. - Rather than storing an unlimited number of
individual video frames, the approach uses the incoming flow of data to
continuously tune the synaptic connections of a multi-connected neural
network. This is analogous to the way associative memorization is
performed in visual cortex. Then, when a new video stimulus is
presented to the retina, the neural network converges to state which is
best described by the past seen/learnt experience.
The second result is related to using video sequences, rather
than individual video frames, which is in accordance
with the developed paradigm. As claimed in our papers, high resolution
is not needed for video-based recognition, as it is not the way
it is in biological systems and may, in fact, result in
overloading the problem. Hence, our video sequences are 160x120,
taken with an off-the-shelf webcam, the face of person in video
occupying 1/16 - 1/4 of an image.
Direct applications of this technology are in:
- Face Recognition for Security: Surveillance, Tracking and
- Face Recognition for Computer-Human Interaction: Perceptual User
- Face Recognition for Video annotation and Games
- General object memorization and recognition in low-quality video