Abstract:
A method is described for selecting an enrollment image or standard from multiple close-up images of a subject such as a human eye. The selected image is the one image that best represents the appearance of that subject to an image processing system which identifies subjects by comparison of later taken images of subject to the selected image.

Description:
BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates to selecting the best master enroll image used for identifying individuals from facial images, and more particularly from images of the eye. The master image may be used either in its original form or it may be used to compute a biometric code, such as an iris code which is stored and used for later comparisons. 
     2. Description of the Prior Art 
     There are several methods known as biometrics for recognizing or identifying an individual from personal biological characteristics. Some of these methods involve imaging of the face or eye and analyzing the facial features, retinal vascular patterns of the eye, or patterns in the iris of the eye. In recent years there has been a demand for more reliable systems to identify individuals, particularly those persons who desire access to a secured area or system. A common example of such a secured system is automated teller machines which allow authorized users to conduct banking transactions. Many of these systems are used by a wide variety of people. Very often these people demand quick as well as accurate identification. 
     A technique for accurately identifying individuals using iris recognition is described in U.S. Pat. No. 4,641,349 to Flom et al. and in U.S. Pat. No. 5,291,560 to Daugman. The systems described in these references require a clear and representative master enroll (template) image of the eye. The master enroll image is the image chosen to represent the unique characteristics of an individual, in this case, the characteristics of an individual&#39;s iris. The master enroll image is used as input to Daugman&#39;s algorithm to generate an iris code. The iris code extracted from the master enroll image is compared to a second iris code extracted from a current image of an individual whenever that individual&#39;s identity needs to be determined or verified. The present invention relates to selecting the best master enroll image to be used in such systems. 
     SUMMARY OF THE INVENTION 
     I provide a reliable, efficient and automatic method for selecting the best master enroll image from a set of close-up images of an eye. I compute focus measures for these images. I select those images having a degree of focus in a range representing the characteristics of the imaging system employed. Each of the above selected images is then verified against the remainder of the selected images. For each of these sets, the number of successful verifications and the median Hamming distances of the successful verifications are recorded. The master enroll image is chosen to be one associated with the set having the largest number of successful verifications. If there is more than one set meeting this qualification, the choice is reduced to one by choosing the master enroll image for the set having the lowest median Hamming distance of the successful verifications. 
    
    
     BRIEF DESCRIPTION OF THE FIGURE 
     FIG. 1 is a flowchart of a preferred embodiment of my method for selecting a master enroll image. 
     FIG. 2 is a block diagram of a present preferred apparatus for selecting a master enroll image. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     Using an image processing system consisting of a computer system with standard video imaging facility, such as a CCD video camera and video frame grabber, I obtain multiple close-up images of an eye. Each image is formed by a set of pixels (picture elements) each having a gray scale value. I then process this set of images according to the method of the present invention. 
     I begin at step 10 in FIG. 1 with a subject present in front of the video imaging facility. At step 12, I collect a set of close-up images of one eye of the subject. Such images contain the full iris and pupil portions of the eye with a resolution of at least 130 pixels across the iris diameter. The images in the set differ in that each is taken with a different focus setting. The required number of images M and the range of focus settings depend on the statistics of performance of the specific video imaging facility. In the case of the Sensar . . . Secure™ Iris Identification System Model 765 R1, an empirical analysis of many eye images obtained with the system was used to determine that 20 to 30 images are sufficient to represent the range of focus variation during normal operation. 
     For the IrisIdent Model 765 system, focus was determined to be the most significant variation affecting the quality of eye images to be used for personal identification. However, other parameters that vary during system operation, such as amount and direction of illumination and distance of the subject from the system, also affect the image quality, and it may be necessary to include more images in the set collected in step 12 while varying these other parameters in specific ranges to represent the variation of these other parameters during normal operation. 
     At step 18 in FIG. 1, a focus measure of each of the M images is computed using the method previously described in U.S. patent application Ser. No. 08/982,364, filed Dec. 2, 1997 entitled &#34;Method of Measuring the Focus of Close-Up Images of Eyes&#34;, which application is incorporated herein by reference. This method begins with an approximate location of the pupil/iris boundary in the image, then measures how sharply this boundary is focused. The measurement is done by computing the median pixel value in a portion of the pupil, and then doing the same for a portion of the iris. These medians are subtracted to get the step size at the pupil/iris boundary. Next the method computes the magnitude of gradients at the pupil/iris boundary and divides an average of the gradient magnitudes by the step size to get the multiplicative inverse of the width of the transition region at the pupil/iris boundary. This latter value is maximum when the focus is optimized. Using this method creates a focus measure computation for images that produces a value between 0 and 1, where 1 indicates an image that is 100% in focus. Statistics of the focus measures of more than 2000 eye images obtained from normal operation of Sensar&#39;s Model 765 system show that a focus measure from 0.30 to 0.60 define those images that produce good results in terms of minimizing hamming distances. Therefore, I select from the set of M images, only those N≦M images with focus measures from 0.30 to 0.60. 
     At step 20, each of the N images is verified against the remaining images, such as according to the method described in U.S. Pat. No. 5,291,560. I will call the image against which others are compared the &#34;candidate master enroll image&#34;. For each of these sets of comparison, the number of the successful verifications and the median of the Hamming distances of the successful verifications are recorded. 
     At step 24, the image or images having the largest number of successful verifications are selected as subset S. If the largest number of successful verifications V is less than 90% of the number of selected images N (step 26), then no image is considered to be representative of the collection of N images and thus no master enroll image can be chosen (step 28). Otherwise, if the subset S has only one member (step 30), then this sole candidate master enroll image is chosen to be the master enroll image (step 32), but if the subset S has more than one member (step 30), then the master enroll image is chosen to be the candidate master enroll image in subset S that produces the smallest median Hamming distance for successful verifications (step 33). The process ends at step 34. 
     The 90% threshold at step 26 is derived empirically and it depends on the focus range chosen in step 18 which in turn depends on the performance of many elements of the image processing system and its associated hardware (camera, lens, frame grabber, etc.) 
     There may be occasions when it is impractical or disagreeable for multiple close-up images of the eye of the subject to be obtained as specified in step 12. In such cases, it is possible to simulate the capture of multiple images at different focus settings. The only required input image is a single sharply focused image of the subject&#39;s eye having resolution of at least 130 pixels across the diameter of the iris. A range of images with various degrees of defocus, representative of the results of a range of focus settings in the actual imaging system, may be produced from the single input image by digitally effecting low-pass spatial filtering of the input images using neighboring averaging (see Digital Image Processing, R. C. Gonzalez and R. E. Woods, Addison-Wesley, 1992). The energy causing the image of a given pixel in the sharply focused input image is spread evenly over a circular neighborhood in a defocused image. The radius of this neighborhood increases with the degree of defocus. (See section 4.3 of &#34;Real-Time Focus Range Sensor&#34; by S. K. Nayar, M. Watanabe, and M. Noguchi in the Proceedings of the International Conference on Computer Vision, June 1995, pp. 995-1001.) Thus neighborhood averaging with circular neighborhoods may be used to generate defocused images from a single sharply focused image. 
     Because of the equivalence of defocusing with a specific kind of low-pass spatial filtering, it is evident that the most sharply focused image of an eye contains the most information about the details of the texture of the iris, and thus about the identity of the subject. But various imperfections in real video capture facilities, especially some degree of defocus, generally cause the loss of high-frequency detail in captured images. The preferred embodiment of FIG. 1 may be viewed as a method of filtering some high-frequency detail from the most sharply focused image of a subject&#39;s eye, which sacrifices some small amount of information about his identity, in order that the result, the master enroll image, better represents the distribution of randomly varying images expected from real video capture facilities. As the imperfections in video capture facilities used for iris identification are reduced, the master enroll image will move closer in Hamming distance and spatial frequency content to a sharply focused image of the subject&#39;s eye. 
     My method can be practiced using a camera connected to a computer which has sufficient processing capability and memory to perform the method. Referring to FIG. 2, I provide a camera 28 which creates a set of digitized images of a subject&#39;s eye 36. This camera preferably is a CMOS imager. The set of images M is transmitted to a first memory 41. A processor 51 retrieves set M and selects from that set a second set of images, set N, which all have a focus value within a predetermined range. That set N is stored in memory 42. A second processor 52, identified as an image comparison processor in the drawing, retrieves the images of set N from memory. That image comparison processor compares each image within set N to all other images in that set and creates a third set S. Set S contains those images in set N which have a predetermined number of successful matches to the other images in set N. Set S is stored in memory 43. A third processor 53 selects the master enroll image 56 from set S. As indicated by the dotted line box 40 each of the memories 41, 42 and 43 may be a single memory device that may contain other information. Similarly a single processing device 50 can be used for processor 51, image comparison processor 52 and image selector 53. The memory 40 and processor 50 preferrably are contained within a computer indicated by dotted line box 60. 
     Although my method is particularly useful for iris identification, it is applicable to other situations involving image matching to identify a subject in one image by comparing it to a previously stored image, here called the enrollment image. For example, the image may contain other biometric features of an individual such as a fingerprint, hand print or full facial image. The method could also be used to identify products by symbols on their packages which are more complex than simple bar codes. The method could also be used to select a master image against which products are compared to assure quality. The features being assessed for quality control purposes may range from dimensions, to print quality, to orientation of labels on a package. 
     The images may be produced using any type of imaging device and lighting. For some applications illumination with visible light may be selected, while infrared, ultraviolet, or X-ray imaging may be used for other situations. I have found infrared illumination of nominally 880 nm. to be preferrable for iris identification because this type of light easily passes through sunglasses. Whatever illumination system is used to take images of a subject to be identified, the illuminators need not be changed to practice the present invention. Indeed, I prefer to create the enrollment image using the same illumination that will be used to capture the images to be compared to the enrollment image. 
     Although I have shown and described certain present preferred embodiments of our method it should be understood that our invention is not limited thereto but may be variously embodied within the scope of the following claims.