Ordinal Measures for Iris Recognition

By Zhenan Sun, Tien-Ping Tan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

325 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

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].”
Semi-supervised tuning from temporal coherence (2015, background): “…scope of this paper, but some hints are given in the following: • Dilobe ordinal ﬁlters : the feature extraction at the ﬁrst level is not based on a variable number of self-learned templates as described in Maltoni (2011), but is carried out with a bank of 50 dilobe ordinal ﬁlters Sun & Tan (2009).”
A review of biometric technology along with trends and prospects (2014, methodology): “Table 3: Summary of ocular region modalities: Modality Feature Set Recognition Techniques Datasets Iris Color, shape and iris texture (crypts, furrows, corona, freckles) 2D Gabor filters [144, 154, 155], Wavelets [156-159], LoG filter [160], DCT [161], Ordinal measures [162, 163], ICA [164], PCA [16”
A Light CNN for Deep Face Representation With Noisy Labels (2015, background): “The binary gradient plays a similar role of ordinal measure [36] that is famous local feature and widely used in biometrics.”