Pigment Melanin: Pattern for Iris Recognition

  title={Pigment Melanin: Pattern for Iris Recognition},
  author={S. Mahdi Hosseini and Babak Nadjar Araabi and Hamid Soltanian-Zadeh},
  journal={IEEE Transactions on Instrumentation and Measurement},
Recognition of iris based on visible light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, which is unavailable in near-infrared (NIR) imaging. This is due to the biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To… 

Figures and Tables from this paper

Iris Recognition: Comparing Visible-Light Lateral and Frontal Illumination to NIR Frontal Illumination
This article presents an image acquisition method that enhances viewing the structural information of the muscle fibers of the iris on the resulting image and produces more separable distributions than those of the NIR device, and much better distribution than using frontal visible-light alone.
Multispectral scleral patterns for ocular biometric recognition
Fusing the information in visible light and near-infrared images for iris recognition
An iris recognition system that uses images acquired in both near-infrared and visible lights and demonstrates the necessity to process both VL and NIR images to recognize irides, and proves that fusion can compensate the lack of input image information.
Iris biometrie: Is the near-infrared spectrum always the best?
Experimental results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves the recognition performance, and the experiments indicate that cross-channel matching between VL-NIR images is feasible.
A novel framework for cross-spectral iris matching
Experimental and comparison results demonstrate that the proposed framework achieves state-of-the-art cross-spectral matching and indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves notably the recognition performance.
An Investigation of Quality Aspects of Noisy Colour Images for Iris Recognition
New quality metrics are presented to assess the image characteristics with regard to focus, entropy, reflections, pupil constriction and pupillary boundary contrast to suggest the existence of different characteristics for these channels and could be exploited for use in the design and evaluation of iris recognition systems.
Reflection removal and feature extraction techniques in non-cooperative visible eye images for iris recognition
The research proposed the development of two combined methods to improve the iris recognition system which identifies and classifies between reflections and nonreflections and uses score combination of multiscale at the end of the process to increase the matching score.
Cross-spectrum periocular authentication for NIR and visible images using bank of statistical filters
An ocular image database collected using the visible and NIR cameras is employed and a new framework employing a bank of Binarized Statistical Image filters along with χ2 distance metric along with simple fusion to handle the cross-spectrum data is proposed.
Ocular recognition databases and competitions: a survey
A survey of the databases created for ocular recognition, detailing their protocols and how their images were acquired and some relevant works applying deep learning techniques to ocular Recognition point out new challenges and future directions.
Cross-Spectral Iris Matching for Surveillance Applications
Two methods for cross-spectral iris recognition capable of matching iris images in different lighting conditions are proposed and results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves the recognition performance.


Shape Analysis of Stroma for Iris Recognition
A new shape analysis approach for iris recognition is proposed which produces a smooth image in which shape of pigmented fibro vascular tissue known as Stroma is depicted easily and an adaptive filter is defined to extract these shapes.
Iris Recognition for Partially Occluded Images: Methodology and Sensitivity Analysis
Two different segmentations of iris are presented and it is observed that relying on a smaller but more reliable part of the iris, though reducing the net amount of information, improves the overall performance.
Feature fusion as a practical solution toward noncooperative iris recognition
Experimental results show that the proposed approach to take iris images at both near infrared and visible light and use them simultaneously for recognition leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.
Improving iris recognition accuracy via cascaded classifiers
A novel cascading scheme is proposed to combine the LFC and an iris blob matcher to overcome the limitations of local feature based classifiers and significantly improve the system's accuracy with negligible extra computational cost.
Personal Identification Based on Iris Texture Analysis
A bank of spatial filters, whose kernels are suitable for iris recognition, is used to capture local characteristics of the iris so as to produce discriminating texture features and results show that the proposed method has an encouraging performance.
Efficient iris recognition by characterizing key local variations
The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris.
Comparison and combination of iris matchers for reliable personal identification
  • Ajay Kumar, Arun Passi
  • Computer Science
    2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
  • 2008
This paper presents a comparative study of the performance from the iris identification using log-Gabor, Haar wavelet, DCT and FFT based features and suggests the combination of these two matchers is most promising, both in terms of performance and the computational complexity.
Iris Recognition: An Entropy-Based Coding Strategy Robust to Noisy Imaging Environments
An entropy-based iris coding strategy that constructs an unidimensional signal from overlapped angular patches of normalized iris images is proposed to avoid the comparison between components corrupted by noise and achieve accurate recognition, even on highly noisy images.
Local intensity variation analysis for iris recognition
Toward Noncooperative Iris Recognition: A Classification Approach Using Multiple Signatures
An iris classification method is proposed that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule.