The DOF problem in iris acquisition systems

This is the third post in the series on Iris acquisition for biometrics. In the first and the second posts we saw that, at least in theory, iris recognition is an ideal biometric, and we went through some of the desirable properties of an iris acquisition system. However, currently most iris recognition systems require a single subject to stand (or move slowly) at a certain standoff distance from the camera in order capture and process iris images. Wouldn’t it be nice if iris recognition could be simultaneously performed for a group of people who may be standing/ moving within a large volume? Such systems could potentially be used in crowded places such as airports, stadiums, railway stations etc.

In this post, we will look at one of the limitations of current iris recognition systems – the limited depth of field, the fundamental cause of this limitation, and how some of the current systems are addressing this problem.

The problem of DOF

The inability of any conventional imaging system to capture sharp images within a large volume is illustrated in the Figure 1.

Figure 1 Depth of field (DOF) problem. Image of the three human-figure cut-outs with sinusoidal patterns (2 lp/mm) and artificial irises and placed apart by 11 cm from each other. The camera, with lens of 80 mm focal length and f/5 aperture, was focused on the middle cut-out (3.6 meters away from the camera). It is evident that the spatial resolution in the image falls off rapidly with increasing distance from the plane of sharp focus (middle cut-out) inhibiting the camera from resolving fine details uniformly across the imaging volume.

Figure 1 Depth of field (DOF) problem. Image of the three human-figure cut-outs with sinusoidal patterns (2 lp/mm) and artificial irises and placed apart by 11 cm from each other. The camera, with lens of 80 mm focal length and f/5 aperture, was focused on the middle cut-out (3.6 meters away from the camera). It is evident that the spatial resolution in the image falls off rapidly with increasing distance from the plane of sharp focus (middle cut-out) inhibiting the camera from resolving fine details uniformly across the imaging volume.

Perfect imaging corresponds to the ability of an imager to produce a scaled replica of an object in the image  space [1].  When only a small portion of the light  wave emerging  from an infinitesimally  small point source of light is collected through a finite opening of a camera’s aperture (Figure 2 (a)), the replica in the image space is not exact even in the absence of aberrations; instead, the image of the point spreads out in space due to diffraction at the aperture. This dispersed response in the three-dimensional image space is called Point Spread Function (PSF).  The spreading of the PSF along   the transverse (xy-axis) direction (a 2D PSF) restricts an imager’s ability to resolve fine details (spatial frequency) in the image. For an extended object, which is made of several points, the 2D PSF smears the responses from neighboring points into each other causing blur. Similarly, the spread along the longitudinal direction (z-axis) limits the ability to discriminate points staggered closely in the direction of the optical axis causing a region of uncertainty; however, the extension of the 3D PSF along the optical axis enables multiple spatially-separated objects (or points) within a volume in the object space to form acceptably sharp images at once.  Conversely, an (point) object in the object space may be placed anywhere within this zone and still form a satisfactory image. This zone of tolerance in the object space is called depth of field. The corresponding zone in the image space is called depth of focus [2]. In this post, the acronym “DOF” is used for both depth of field and depth of focus wherever its meaning is apparent from the context. In the image space, the DOF is defined as the region of the 3D PSF where the intensity is above 80% of the central maximum [3,4]. This zone is in the shape of a prolate spheroid. In the absence of aberrations, the maximum intensity occurs at the geometric focal point, z_g, where contributions from all parts of the pupil are all in phase. Figure 2 (b) shows the aberration-free intensity distribution, I_n(r, \delta z), as a function of defocus \delta z = z_i - z_g about the geometric focal point for a light source placed at 100 millimeters from a lens of focal-length of 25 mm and aperture diameter of 5 mm. The expression for the distribution—normalized to make I_n(0,0) equal to unity—is obtained using scalar diffraction theory and paraxial assumptions.

Figure 2 Incoherent impulse response and DOF. (a) The image A’ of a point source A spreads out in space forming a zone of tolerance called Depth of Focus (DOF) in the image space; (b) The normalized focal intensity distribution of the 3D PSF of a 25mm, f/5 lens imaging an axial point source at a distance of 100mm. The expression for the 3D PSF was obtained for a circular aperture using scalar diffraction theory and paraxial assumption. The DOF, having prolate spheroidal shape, is defined as the region within which the intensity has above 80% of the intensity at the geometric focus point. The figure shows iso-surfaces representing 0.8, 0.2, 0.05 and 0.01 intensity levels. The ticks on the left vertical side indicate the locations of the first zeroes of the Airy pattern in the focal plane. The vertical axis has been exaggerated by 10 times in order to improve the display of the distribution.

Figure 2 Incoherent impulse response and DOF. (a) The image A’ of a point source A spreads out in space forming a zone of tolerance called Depth of Focus (DOF) in the image space; (b) The normalized focal intensity distribution of the 3D PSF of a 25mm, f/5 lens imaging an axial point source at a distance of 100mm. The expression for the 3D PSF was obtained for a circular aperture using scalar diffraction theory and paraxial assumption. The DOF, having prolate spheroidal shape, is defined as the region within which the intensity has above 80% of the intensity at the geometric focus point. The figure shows iso-surfaces representing 0.8, 0.2, 0.05 and 0.01 intensity levels. The ticks on the left vertical side indicate the locations of the first zeroes of the Airy pattern in the focal plane. The vertical axis has been exaggerated by 10 times in order to improve the display of the distribution.

The shape—length and breadth—of the 80% intensity region (Figure 2(b)) dictates the quality of the image acquired by an imager in terms of lateral spatial resolution and DOF.

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Desirable properties of iris acquisition systems

In my last post, I described briefly how iris recognition works. I also described the four main modules that make up a general iris recognition system. In this post I am going to discuss some of the desirable properties of the iris acquisition module, or simply the iris camera. Although the acquisition module is the first block in the iris authentication/verification pipeline and it plays a very important role, the module has received much less attention of the researchers compared to the others.

Iris recognition algorithms have become quite mature and robust in past decade due to the rapid expansion of research both in industry and academia  [1–3]. Figure 1 shows a plot of the scientific publications (in English) on iris recognition between 1990 and 2013. The plot also shows the relative number of papers exclusively addressing the problems of the acquisition module, which is really very minuscule, compared to the total number of papers on iris recognition.

Literature survey

Figure 1. Number of publications in (English) journals on iris recognition between 1990 and 2013. The data was collected using Google scholar by searching the keywords IRIS + RECOGNITION + ACQUISITION + SEGMENTATION + NORMALIZATION + MATCHING. The plot shows that although the total number of research papers on iris recognition has grown tremendously during the last decade, the problems associated with iris acquisition have been overlooked.

The accuracy of iris recognition is highly dependent on the quality of iris images captured by the acquisition module. The key design constraints of the acquisition system are spatial resolution, standoff distance (the distance between the front of the lens and the subject), capture volume, subject motion, subject gaze direction and ambient environment [4]. Perhaps the most important of these are spatial resolution, standoff distance, and capture volume. They are described in details in the following paragraphs.

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Primer on iris recognition

As part of my PhD research, I am working on  extending the depth-of-field of iris recognition cameras. In a series of blog posts (in the near future) I would like to share some of the things that I have learnt during the project. This post, the first one in the series, is an introduction to iris recognition biometric technology. I believe the material presented here could benefit someone new to iris recognition get a quick yet comprehensive overview of the field. In the following paragraphs, I have described the iris anatomy, and what makes it so special as a biometric technology, followed by the general basis of iris based verification, and the four major constituents of a general iris recognition system.

The human iris is the colored portion of the eye having a diameter which ranges between 10 mm and 13 mm [1,2]. The iris is perhaps the most complex tissue structure in the human body that is visible externally. The iris pattern has most of the desirable properties of an ideal biomarker, such as uniqueness, stability over time, and relatively easy accessibility. Being an internal organ, it is also protected from damage due to injuries and/or intentional fudging [3]. The presence or absence of specific features in the iris is largely determined by heredity (based on genetics); however the spatial distribution of the cells that form a particular iris pattern during embryonic development is highly chaotic. This pseudo-random morphogenesis, which is determined by epigenetic factors, results in unique patterns of the irises in all individuals including that of identical twins [2,4,5]. Even the iris patterns of the two eyes from the same individual are largely different. The diverse microstructures in the iris that manifest at multiple spatial scales [6] are shown in Figure 1. These textures, unique to each eye, provide distinctive biometric traits that are encoded by an iris recognition system into distinctive templates for the purpose of identity authentication. It is important to note that the color of the iris is not used as a biomarker since it is determined by genetics, which is not sufficiently discriminative.

Iris Anatomy

Figure 1. Complexity and uniqueness of human iris. Fine textures on the iris forms unique biometric patterns which are encoded by iris recognition systems. (Original image processed to emphasize features).

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