Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns

By John G. Daugman. International Journal of Computer Vision, 2001

504 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/eb240521d008d582af37f0497f12c51f4bab16c8

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Citations in the corpus

Efficient iris recognition by characterizing key local variations (2004, background): “In the next stage, we will associate the current features with global texture information for more efficient and robust iris recognition.”
Personal Identification Based on Iris Texture Analysis (2003, background): “These visible characteristics, which are generally called the texture of the iris, are unique to each subject [5], [6], [7], [12], [15], [16], [17], [18], [19], [20], [21].”
DCT-Based Iris Recognition (2007, methodology): “It is known that images of the human iris obtained with Near-Infrared (NIR) lighting are necessary to reveal complex textures for darkly pigmented irises, while lighter irises can be imaged either in the infrared or visible spectrum [12].”
Modern Image Quality Assessment (2006, background): “First, it has been pointed out that Fourier phase carries more important information about image structures than Fourier magnitude [61], and indeed, wavelet phase has been successfully applied in a number of machine vision and image-processing applications [62–64].”
Complex Wavelet Structural Similarity: A New Image Similarity Index (2009, background): “Wavelet phase has also been employed in a number of machine vision and image processing applications [36]–[38].”
Ordinal Measures for Iris Recognition (2009, methodology): “" Many iris recognition methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17] have been reported, but these questions remain open and intriguing.”
Image understanding for iris biometrics: A survey (2008, methodology): “Daugman suggested that image focus could be measured by calculating the total high frequency power in the 2D Fourier spectrum of the image [31,33].”
Combining Face and Iris Biometrics for Identity Verification (2003, background): “There are four main approaches to iris representation: phase information coding [13][16], zero-crossings representation [24], shape description [25] and texture analysis [14-15][25-28].”