Self-adaptive iris image acquisition system
Wenbo Dong, Zhenan Sun, Tieniu Tan, Xianchao Qiu
National Laboratory of Pattern Recognition, Institute of Automation, Academy of Sciences,
No.95 Zhongguancun East Road, Beijing, China
ABSTRACT
Iris image acquisition is the fundamental step of the iris recognition, but capturing high-resolution iris images
in real-time is very difficult. The most common systems have small capture volume and demand users to fully
cooperate with machines, which has become the bottleneck of iris recognition’s application. In this paper, we aim
at building an active iris image acquiring system which is self-adaptive to users. Two low resolution cameras are
co-located in a pan-tilt-unit (PTU), for face and iris image acquisition respectively. Once the face camera detects
face region in real-time video, the system controls the PTU to move towards the eye region and automatically
zooms, until the iris camera captures an clear iris image for recognition. Compared with other similar works, our
contribution is that we use low-resolution cameras, which can transmit image data much faster and are much
cheaper than the high-resolution cameras. In the system, we use Haar-like cascaded feature to detect faces and
eyes, linear transformation to predict the iris camera’s position, and simple heuristic PTU control method to
track eyes. A prototype device has been established, and experiments show that our system can automatically
capture high-quality iris image in the range of 0.6m×0.4m×0.4m in average 3 to 5 seconds.
Keywords: biometrics, iris recognition, image acquisition, pan-tilt-zoom camera, face detection, auto zoom
1. INTRODUCTION
Iris recognition is one of the most reliable methods for personal identification, and potential to be used in many
mission-critical applications. [1][2] However, the size of iris is small (11mm), but the number of iris diameter
pixels is large (normally 150 pixels required for recognition). Furthermore, iris texture only exhibits in infrared
illumination condition, especially for Asian people. So, it is very difficult to capture iris images in practice.
Many commercial products have been developed by some companies such as Iridian[3], OKI[4], Matsushita[5],
LG[6],etc.. Most of the products are non-contacting and acquire iris images at a distance. For aiming at eyes at
a distance, systems need users to cooperate with the machine actively, such as stare at the camera or move again
and again under instructions, but sometimes it is difficult for users to self-locate their positions, especially for
beginners. Although those devices are equipped with cold mirrors, monitors, sound indicators, or LED indicators
to guide subject’s movements, some users still can not perform well. This problem nearly becomes the bottleneck
of iris recognition’s application.
Recently, many people have advanced automatic iris image acquisition systems based on some new meth-
ods.(for example, systems of Sarnoff[7], Mitsubishi[8] and Yongsei University[15]) They used one or two wide-angle
cameras to find subject’s face, and then another narrow-angle camera with high-resolution (more than 4 mega
pixels) to capture the iris image. The iris camera were set up on the pan-tilt unit and controlled to rotate towards
eye region . This method is an important reference to us.
Besides this method, Sarnoff corp. has developed the system of “Iris on the move”.[9][10] They did not use
the movable cameras, but used a camera array made up with many high-resolution cameras (2048×2048 pixels,
15 frames per second). When people walked through a portal equipped with illumination lamps, his eyes would
affirmatively appear in one of the cameras immediately.
Both the two methods use high resolution cameras (more than 4 mega pixels),but CCD with more pixels
transfers less frame per second. Furthermore, the high resolution with high frame rate is very expensive. We
Further author information: (Send correspondence to Wenbo Dong)
Wenbo Dong: E-mail: wbdong@nlpr.ia.ac.cn, Telephone: 086 010 62632265

Figure 1. The appearance of the prototype
should look for a tradeoff between high resolution images and devices’ price. Is the high resolution cameras the
only option or the best option?
In this paper, We use two low resolution cameras (less than 0.5 mega pixels), which are co-located in a PTU,
for face and iris image acquisition respectively. Once the face camera detects face region in real-time video, the
PTU is controlled to move towards eye region and adjust the lens focus at the same time, until the iris camera
captures clear iris image for recognition. Experiments show our system can capture iris image automatically in
the volume of about 0.6m×0.4m×0.4m during average 3-5 seconds(2.7 seconds in the best position).
Our work gave an attempted on non-cooperative iris image acquisition using low resolution cameras, and
achieves the same tracking accuracy with the previous techniques. The tracking speed is not very fast, but
the image frame rate is fast enough to process images in the real-time, and the design can lower the expense.
Moreover, we use Haar-like cascaded feature to detect faces and eyes, linear transformation to predict the
iris camera’s position, and simple heuristic PTU control method to track eyes. A prototype device has been
established. Those are our contributions in this paper.
The paper is structured as follow: The next section describes the overview of the system hardware and
software design; section 3 introduces many technique details; while, section 4 and 5 presents the experiment
results and conclusions.
2. OVERVIEW OF THE SYSTEM HARDWARE AND SOFTWARE
2.1 Installation
The devices include one wide-angle camera(W-Cam), one narrow-angle camera (N-Cam), infrared lamps, PTU
and a computer.
The two cameras and infrared lamps are installed in PTU and rotated together with the PTU. (Fig. 2) The
wide-angle camera captures face images, and the narrow-angle camera captures eye images. The two cameras
are fixed tightly and nearly coaxially. When an eye image appears in a reference point in the W-Cam’s center,
the eye image will appear in N-Cam’s view. The reference point’s value is calibrated on installation.
For illumination, we use the infrared LED array with 20 degree emission angle, which is installed on the PTU
and rotated with the two cameras together.
Figure. 1 is the appearance of the prototype device we have established.