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Perceptual Signatures:
- Perceptual signatures encode identifiers implicit in our facial
features, gaits, communication styles, behaviors, daily activities, etc.
- Perceptual signatures research has produced innovative algorithms
based on statistical machine learning and numerical tensor algebra
for extracting perceptual signatures from large datasets, generated
by individuals or groups of people, corporations, government
agencies, terrorist cells, etc.
- Perceptual signatures come in a variety of forms, such as a spending
profile that banks can use to identify fraudulent spending, a
characteristic gesture that comedians exploit, a human motion
signature for computer animation, a facial signature that can be
identified by keyless access systems, etc.
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Biometric Computing using Perceptual Signatures:
Perceptual signatures distilled from biometric data is fundamental to
human-centric technologies such as the burgeoning security industry,
but when directed at ourselves, biometric technologies can serve as
reflectors that enhance our self-awareness, understanding, and health,
and they can facilitate our interaction with each other and
computers.
- Reflective Biometrics distills biometric data to perceptual
signatures for self-surveillance. This can enable self-monitoring
(the sci-fi Heechee Saga - the future for at-home health care.)
Self-surveillance systems can become instruments of the individual. Reflective biometrics research is the self-examination via technology as a mirror.
- TensorFaces for recognition is an individual's identifier
extracted from unconstrained facial images that can confuse and
mislead facial recognition systems. Tensor representations of facial
images enable robust facial recognition under unconstrained
viewpoint, illumination, expression, and other conditions.
- Human Motion Signatures is a quantitative model of human motion
that can be used to identify an individual or characterize the gait
as normal versus pathological. Multilinear algebra is applied to the
nonlinear representation, analysis, synthesis, and recognition of
human movement from perceptual data.
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