Iris Recognition Based on Human-Interpretable Features

ABSTRACT:

The iris is a stable biometric trait that has been widely used for human recognition in various applications. However, deployment of iris recognition in forensic applications has not been reported. A primary reason is the lack of human-friendly techniques for iris comparison. To further promote the use of iris recognition in forensics, the similarity between irises should be made visualizable and interpretable. Recently, a human-in-the-loop iris recognition system was developed, based on detecting and matching iris crypts. Building on this frame-work, we propose a new approach for detecting and matching iris crypts automatically. Our detection method is able to capture iris crypts of various sizes. Our matching scheme is designed to handle potential topological changes in the detection of the same crypt in different images. Our approach outperforms the known visible-feature-based iris recognition method on three different data sets. In particular, our approach achieves over 22% higher rank one hit rate in identification, and over 51% lower equal error rate in verification. In addition, the benefit of our approach on multi-enrollment is experimentally demonstrated.

PROJECT OUTPUT VIDEO:

EXISTING SYSTEM:

• In existing system for identification, the probe image under investigation is first processed by the system to detect visible features automatically (the Analysis step). A dissimilarity score between the probe image and each gallery image is computed (the Comparison step). The system will retrieve m candidate images from the gallery whose features have the most similar patterns to the probe image, i.e., the smallest dissimilarity score (the Evaluation step). In practice, m is a small integer, such as 10 or 20. Finally, human examiners will manually compare the candidate images against the probe image with the human-interpretable features labeled and the similarity between the features in the probe image and different candidate images presented, so as to make the conclusion on the identity of the probe image (the Verification step).

• In verification applications, the system processes the probe image to detect features first (Analysis). The dissimilarity score between the probe image and the gallery image(s) of the identity that the probe image claims to be is computed (Comparison). The system will present the results to human examiners, only if the dissimilarity score is lower than a threshold (Evaluation). The human examiners will inspect the results, with the aid of detected features and similarity between corresponding features, to accept or reject that the probe image has the claimed identity.

DISADVANTAGES OF EXISTING SYSTEM:

• The existing system decreases the reliability.

• There are very few results on investigating iris recognition using human-friendly features.

PROPOSED SYSTEM:

• In this paper, we seek to improve the performance of the automated iris recognition process, i.e., the first three steps of the ACE-V framework. Specifically, we propose a new fully automated approach to: (1) extract human-interpretable features in iris images, and (2) match the features with the images in the database to determine the identity. Our proposed approach can provide reliable aid to human evaluation in a human-in-the-loop iris recognition system.

• Our new approach employs the following observations. In theory, iris crypts may appear in various sizes and shapes in images. In practice, it is sometimes uncertain whether multiple proximal crypts are connected. Furthermore, slight differences in the acquired images of the same iris may alter the topology of the detection of the same crypts from image to image. The two images are from the same eye, but acquired at different times. Examples of the same crypts with different topologies are labeled in the red boxes. Yet, even though the topology of particular crypts may vary, the overall similarity can still be determined quite easily by a human examiner.

• There are two main tasks in our approach: crypt detection and crypt matching. Our detection (or segmentation) algorithm is designed to handle multi-scale crypts. It applies a key morphological operation in a hierarchical manner. Human annotated training data is used to determine the major parameters, so that the detected crypts are similar to those obtained by human inspection.

• In our matching algorithm, we adopt a matching model based on the Earth Mover’s Distance (EMD). This matching model is quite general. Specifically, to handle possible differences in crypt topology, our matching algorithm is able to establish correspondences between the detected crypts in two images, which can be one-to-one, one-to-multiple, multiple-to-one, or even multiple-to-multiple matching. Additionally, due to different lighting conditions, there may be some false alarms or missing detections. Not all crypts can be captured in every image, subject to different physical conditions. Our matching algorithm is carefully designed so that it performs robustly to segmentation errors and potential appearance/disappearance of small crypts.

ADVANTAGES OF PROPOSED SYSTEM:

• Our system increase the reliability of the human-in-the-loop iris biometric system, incorporating a quality measure for images enrolled in the system would be beneficial.

• This would allow to evaluate whether the quality of each acquired image is good enough for visual feature matching. Based on our observations and trial studies, our approach is robust with respect to certain common factors, such as interlacing or moderate blurring.

MODULES:

• Input Image
• Localization
• Normalization
• Gaussian filter
• Morphological operation
• Binary Image
• Segmentation

MODULES DESCRIPTION:

Input Image:

To read the image from given folder select pathname and filename specified format using matlab syntax (imread).

Localization:

Both inner boundary and out boundary of typical iris can be taken as circle. But both two circles are usually not co-centric. Also inner circle will be detecting pupil in iris. The outer boundary of circle more difficult to detect because the boundary level contrast very low.

Normalization:

The size of the pupil due to the change of variation and illumination elastic deformation. Iris texture may interfere the result of pattern matching. Then also the inner boundaries also outer boundaries easy to detect map in iris ring.

Gaussian filter:

Gaussian filter is a filter whose impulse response is a Gaussian function (or an approximation to it). Gaussian filters have the properties of having no overshoot to a step function input while minimizing the rise and fall time. This behavior is closely connected to the fact that the Gaussian filter has the minimum possible group delay. It is considered the ideal time domain filter, just as the since is the ideal frequency domain filter .These properties are important in areas such as oscilloscopes and digital telecommunication systems.

Morphological operation:

Morphological image processing is a collection of non-linear operations related to the shape or morphology of features in an image. According morphological operations rely only on the relative ordering of pixel values, not on their numerical values, and therefore are especially suited to the processing of binary images. Morphological operations can also be applied to grayscale images such that their light transfer functions are unknown and therefore their absolute pixel values are of no or minor interest. Morphological techniques probe an image with a small shape or template called a structuring element. The structuring element is positioned at all possible locations in the image and it is compared with the corresponding neighborhood of pixels. Some operations test whether the element “fits” within the neighborhood, while others test whether it “hits” or intersects the neighborhood.

Binary Image:

A binary image is a digital image that has only two possible values for each pixel. Typically, the two colors used for a binary image are black and white, though any two colors can be used. The color used for the object(s) in the image is the foreground color while the rest of the image is the background color. In the document-scanning industry, this is often referred to as “bi-tonal”. Binary images are also called bi-level or two-level.

This means that each pixel is stored as a single bit—i.e., a 0 or 1. The names black-and-white, B&W, monochrome or monochromatic are often used for this concept, but may also designate any images that have only one sample per pixel, such as grayscale images. operations such as segmentation, thresholding, and dithering. Some input/output devices, such as laser printers, fax machines, and bilevel computer displays, can only handle bilevel images. A binary image can be stored in memory as a bitmap, a packed array of bits. A 640×480 image requires 37.5 KiB of storage. Because of the small size of the image files, fax machine and document management solutions usually use this format. Most binary images also compress well with simple run-length compression schemes.

Segmentation:

In computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as super-pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. More precisely, image segmentation is the process of assigning a label to every pixel in an image such that pixels with the same label share certain characteristics.

The result of image segmentation is a set of segments that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as color, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s) When applied to a stack of images, typical in medical imaging, the resulting contours after image segmentation can be used to create 3D reconstructions with the help of interpolation algorithms like Marching cubes.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

• System            : Pentium Dual Core.

• Hard Disk       : 120 GB.

• Monitor          : 15’’ LED

• Input Devices : Keyboard, Mouse

• Ram                : 1GB.

SOFTWARE REQUIREMENTS:

• Operating system : Windows 7.

• Coding Language : MATLAB

• Tool                      : MATLAB R2013A

REFERENCE:

Jianxu Chen, Feng Shen, Danny Ziyi Chen, Fellow, IEEE, and Patrick J. Flynn, Fellow, IEEE, “Iris Recognition Based on Human-Interpretable Features”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 11, NO. 7, JULY 2016.