Personal Identification Based on Iris Texture Analysis

By Li Ma, Tien-Ping Tan, Yunhong Wang, Dexin Zhang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003

1,105 citations recorded by the source corpus.

Subfields: Genetics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c733ea94a83e9696a12ac1f17773a5623b6dcd0f

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Citations in the corpus

DCT-Based Iris Recognition (2007, methodology): “[16], [17], and Monro and Zhang [18] have contributed new methods including ones based on the application of PCA and ICA to iris images [19], [20].”
Secure and Robust Iris Recognition Using Random Projections and Sparse Representations (2011, neutral): “to characterize blur and occlusion [15].”
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, methodology): “Ma et al. [12], [13] used Hough transform to detect the iris and pupil boundaries.”
UBIRIS: A Noisy Iris Image Database (2005, methodology): “This methodology is the most widely used, being proposed with minor several variants in [3], [11], [14], [ 17 ], [19], [18] and [20].”
Palmprint verification based on principal lines (2008, methodology): “Compared with fingerprint or iris based personal verification systems which have been widely used [2] [3], the PVS can also achieve satisfying performance.”
Detecting Fertility and Early Embryo Development of Chicken Eggs Using Near-Infrared Hyperspectral Imaging (2013, background): “An oriented Gabor filter G0, which is a Gaussian function modulated by an oriented harmonic function, can be defined as follows (Ma et al. 2002): G0 x; y; f ;σ; θð Þ ¼ 12pσ2 exp x2 þ y2 2σ2 cos 2pf x cos θþ y sin θð Þ½ ; ð1Þ where (x, y) is the coordinate of point in 2-”
Biometrics: a tool for information security (2006, background): “The complex iris texture carries very distinctive information useful for personal recognition [11], [71], [72].”
Ordinal Measures for Iris Recognition (2009, methodology): “Ma et al. proposed describing iris features using texture analysis, based on a bank of spatial filters [5].”
Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition (2010, methodology): “We have addressed many issues in (non-cooperative) iris recognition such as image quality evaluation [5], iris segmentation [6], feature extraction and classification [7–9], coarse iris classification [10], iris spoof detection [11] and iris image synthesis [12].”
Image understanding for iris biometrics: A survey (2008, methodology): “[91] suggested analyzing multiple images and keeping the best-quality image.”
A review of biometric technology along with trends and prospects (2014, methodology): “Table 4: Summary of behavioral modalities: Modality Feature Set Recognition Techniques Datasets Voice Spectrum, Glottal pulse features, pitch, energy, duration, rhythm, temporal features, phones, idiolects, semantics, accent, pronunciation, LPCC [204], MFCC [205], VQ [206-208], HMM [209], GMM [210, ”
Person Identification Using Full-Body Motion and Anthropometric Biometrics from Kinect Videos (2012, background): “For example, high impact research has been conducted that identify people with similar facial, fingerprint, or iris biometrics [1–6] in 2D images or videos.”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, background): “Typical applications of texture analysis include medical image analysis [48, 158, 177], quality inspection [250], content based image retrieval [148, 216, 266], analysis of satellite or aerial imagery [96, 83], face analysis [4, 49, 215, 265], biometrics [137, 185], object recognition [210, 173, 263”
Comparison and combination of iris matchers for reliable personal authentication (2010, methodology): “However, it is very difficult to compare our results with this work since only a portion of CASIA I database is publicly made available and all of which has been employed in our work (while [5] [33] utilize full version of this database).”