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CVPR
2011
IEEE
13 years 3 months ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
ICPR
2006
IEEE
14 years 8 months ago
Face Recognition Robust to Head Pose from One Sample Image
Most face recognition systems only work well under quite constrained environments. In particular, the illumination conditions, facial expressions and head pose must be tightly con...
Brian C. Lovell, Shaokang Chen, Ting Shan
ICARCV
2006
IEEE
420views Robotics» more  ICARCV 2006»
14 years 1 months ago
Recognizing People's Faces: from Human to Machine Vision
— As confirmed by recent neurophysiological studies, the use of dynamic information is extremely important for humans in visual perception of biological forms and motion. Apart ...
Massimo Tistarelli, Manuele Bicego, Enrico Grosso
ECCV
1994
Springer
14 years 9 months ago
Face Recognition: the Problem of Compensating for Changes in Illumination Direction
A face recognition system must recognize a face from a novel image despite the variations between images of the same face. A common approach to overcoming image variations because...
Yael Moses, Yael Adini, Shimon Ullman
AVBPA
2003
Springer
582views Biometrics» more  AVBPA 2003»
14 years 26 days ago
Performance Evaluation of Face Recognition Algorithms on the Asian Face Database, KFDB
Human face is one of the most common and useful keys to a person’s identity. Many algorithms have been developed for automatic face recognition. And a number of commercial produc...
Bon-Woo Hwang, Hyeran Byun, Myung-Cheol Roh, Seong...