DCT-Based Iris Recognition

By Donald M. Monro, Soumyadip Rakshit, Dexin Zhang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007

514 citations recorded by the source corpus.

Subfields: Genetics, Molecular Biology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/416cee835043a49f9a61635c975f3fd2ff16342d

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Citations in the corpus

Image understanding for iris biometrics: A survey (2008, methodology): “Monro, 2007 [102] 2156 CASIA images and 2955 U.”
Comparison and combination of iris matchers for reliable personal authentication (2010, unknown): “1 Discrete Cosine Transform The iris recognition using the phase information from the zero crossings of the one dimensional DCT has shown promising results in [3].”
Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing (2008, methodology): “Further, - Intensity based - 1D log polar Gabor and Euler number + Rule based decision detection Hamming distance and L1 distance strategy is used to improve accuracy Monro et al. [24] and thresholds 2D filter distance and harmonic mean - Active contours and - Gaze deviation correction, Daugman [32]”
A review of biometric technology along with trends and prospects (2014, methodology): “Table 1: Summary of hand region modalities: Modality Feature Set Recognition Techniques Datasets Fingerprint Ridge flow, ridge pattern, singular points, ridge skeleton, ridge flow, ridge ending, ridge contours, ridge kernel, orientation field, island, spur, crossover, learned feature, sweat pores, d”
Influence of Iris Template Aging on Recognition Reliability (2013, methodology): “MIRLIN derives the iris features from the zero-crossings of the differences between Discrete Cosine Transform (DCT) calculated in rectangular iris image subregions [15].”
Ordinal Measures for Iris Recognition (2009, background): “…[11], Haar wavelet binary features [12], correlation filters [13], Gaussian-Hermite moments [14], local extreme points [15], discrete cosine transform [16], direction of gradient vector field [17], etc. Great progress has been made on iris feature representation , but it is still an open problem.”