Efficient iris recognition by characterizing key local variations

By Li Ma, Tien-Ping Tan, Yunhong Wang, Dexin Zhang. IEEE Transactions on Image Processing, 2004

1,090 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/c9e1579d4ed332ebd7012b761877ac132f352101

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In Computer Vision and Pattern Recognition

In Signal Processing

Citations in the corpus

Ordinal Measures for Iris Recognition (2009, background): “" 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.”
DCT-Based Iris Recognition (2007, methodology): “Recently, other researchers including Wildes [13], Boles and Boashash [14], [15], Ma et al. [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].”
Toward Accurate and Fast Iris Segmentation for Iris Biometrics (2009, neutral): “It is reported that most failures to match in iris recognition result from inaccurate iris segmentation [7].”
Iris Segmentation Using Geodesic Active Contours (2009, background): “Only very few algorithms in the literature do not assume circular or elliptical boundaries (e.g., see Abhyankar and Schuckers [16] and Daugman [3]).”
Combining Face and Iris Biometrics for Identity Verification (2003, background): “For more details of the iris recognition system, see [ 9 ].”
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, ”
An Effective Approach for Iris Recognition Using Phase-Based Image Matching (2008, background): “Among many biometric techniques, iris recognition is one of the most promising approaches due to its high reliability for personal identification [1], [2], [3], [4], [5], [6], [7], [8], [9].”
Secure and Robust Iris Recognition Using Random Projections and Sparse Representations (2011, methodology): “[19] also enrolled three iris images and averaged the three Hamming distances to obtain the final score.”
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].”
Estimating tree and stand variables in a Corsican Pine woodland from terrestrial laser scanner data (2009, methodology): “The circular Hough transformation, an image processing technique commonly used for iris detection from digital images (Ma et al. 2004, Kawaguchi et al. 2005), has been used as a method of identifying stem locations from a cross-section of a point cloud (Simonse et al. 2003, Aschoff and Spiecker…”
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.”
Comparison and combination of iris matchers for reliable personal authentication (2010, methodology): “The experimental results in [5] achieve EER of 0.”
Image understanding for iris biometrics: A survey (2008, methodology): “The fusion of results from this method in combination with the results from either Daugman’s [29] or Ma’s [92] methods gives an improvement in performance.”