Iris synthesis: a reverse subdivision application
GRAPHITE '05 Proceedings of the 3rd international conference on Computer graphics and interactive techniques in Australasia and South East Asia
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric technologies and applications
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Image understanding for iris biometrics: A survey
Computer Vision and Image Understanding
Photorealistic models for pupil light reflex and iridal pattern deformation
ACM Transactions on Graphics (TOG)
NETRA: interactive display for estimating refractive errors and focal range
ACM SIGGRAPH 2010 papers
Robust person identification system using iris
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Technical Section: A multiresolution approach to iris synthesis
Computers and Graphics
A model based, anatomy based method for synthesizing iris images
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Synthetic on-line signature generation. Part I: Methodology and algorithms
Pattern Recognition
Iris recognition using consistent corner optical flow
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Computer Vision and Image Understanding
A novel hand reconstruction approach and its application to vulnerability assessment
Information Sciences: an International Journal
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It is very important for the performance evaluation of iris recognition algorithms to construct very large iris databases. However, limited by the real conditions, there are no very large common iris databases now. In this paper, an iris image synthesis method based on Principal Component Analysis (PCA) and super-resolution is proposed. The iris recognition algorithm based on PCA is first introduced and then, iris image synthesis method is presented. The synthesis method first constructs coarse iris images with the given coefficients. Then, synthesized iris images are enhanced using super-resolution. Through controlling the coefficients, we can create many iris images with specified classes. Extensive experiments show that the synthesized iris images have satisfactory cluster and the synthesized iris databases can be very large.