M. Alex O. Vasilescu  

maov@media.mit.edu   

 


 
 
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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.


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.
 


M. Alex O. Vasilescu


   
M. Alex O. Vasilescu  

maov@media.mit.edu   

 


 
 
Home
 
Research
 
Publications
 
Students
 
Courses
 
Awards/Grants/News
 
Media Coverage
 
Downloads
 
Urops
  

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.


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 Signaturess 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.