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Human Recognition-Biometrics

Face Recognition

 

First row: Low resolution input Video

Second row: Super resolution output Video

Third row: Low resolution input Video

Fourth row: Super resolution output Video

  We propose a new method for enhancing the resolution of low-resolution facial image by handling the facial image non-uniformly. We segment facial image into different regions corresponding to different motion models and estimate the motions non-uniformly of tracked regions in the consecutive frames. The experimental results provide a proof of the concept for our method and show that our method gives better results than handling the face uniformly.  



Ear Recognition



Fingerprint Recognition



Predicting Fingerprint Performance

  Binomial prediction model Experimental results for the binomial prediction model: 
(a) Distribution of the match score. 
(b) Distribution of the non-match score. 
(c) Absolute error between the experimental and predicted verification performance. 
(d) Absolute error between the experimental and predicted identification performance.
 



Gait Recognition



Facial Expression

  Unlike current research on facial expression recognition that generally selects visually meaningful feature by hands, our learning method can discover the features automatically in a genetic programming-based approach that uses Gabor wavelet representation for primitive features and linear/nonlinear operators to synthesize new features.  



Face Profile Recognition

 
  Resolution enhancement algorithm is first employed to construct a high-resolution face profile image from multiple low-resolution face profile images that are extracted from video.  



Audio/Video Sensor Fusion

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