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First row: Low resolution input 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. |
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Predicting Fingerprint Performance
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| 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. |
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| 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. |
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| 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. |
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