Open Access
Frequency Domain Representations for Three Dimentional Face Recognition.
Shalini Gupta,Mia K. Markey,Alan C. Bovik +2 more
- pp 252-254
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The article was published on 2008-01-01 and is currently open access. It has received 1 citations till now. The article focuses on the topics: Facial recognition system & Frequency domain.read more
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Journal ArticleDOI
Image quality assessment: from error visibility to structural similarity
TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Journal ArticleDOI
A universal image quality index
Zhou Wang,Alan C. Bovik +1 more
TL;DR: Although the new index is mathematically defined and no human visual system model is explicitly employed, experiments on various image distortion types indicate that it performs significantly better than the widely used distortion metric mean squared error.
Proceedings ArticleDOI
Translation insensitive image similarity in complex wavelet domain
Zhou Wang,Eero P. Simoncelli +1 more
TL;DR: A complex wavelet domain image similarity measure, which is simultaneously insensitive to luminance change, contrast change and spatial translation, is proposed, which can evaluate the similarity of two images without a precise registration process at the front end.
BookDOI
Pattern recognition theory and applications
Pierre A. Devijver,Josef Kittler +1 more
TL;DR: Current research emphasis in pattern recognition is on designing efficient algorithms, studying small sample properties of various estimators and decision rules, implementing the algorithms on novel computer architecture, and incorporating context and domain-specific knowledge in decision making.
Proceedings ArticleDOI
3D Face Recognition Founded on the Structural Diversity of Human Faces
TL;DR: It is shown that incorporating domain specific knowledge about the structural diversity of human faces significantly improves the performance of 3D human face recognition algorithms.
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