J.G. Daugman, High confidence visual recognition of persons by a test of statistical significance, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11) (1993) 1148-1161.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....in HASoC, and the application of UML to the development of complete embedded systems. The chosen example is a distributed system for use in secure environments and consists of a set of door units and a central administrative server. The door units provide secure access, based on iris recognition [20]. Individuals who are to be granted access to secure areas are registered with the system. This requires the capture of iris images, the extraction of iris codes, and the storage of codes in a secure, remote database located on an administrative server. In this paper, we will concentrate on the ....

J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), November 1993.

..... For each image X, we obtain a feature vector Y by projecting X onto the subspace generated by the principal directions. Images are then compared by means of their corresponding feature vectors. 2. 2 Iris Verification Iris recognition is receiving increased attention due to its high reliability [5]. The human iris is an annular region between the black pupil and the white sclera. The texture of iris are unique to each subject [5] 10] Daugman uses texture of iris image as features for classification[5] The iris is first localized with two circles in the image. Then the iris part is ....

....then compared by means of their corresponding feature vectors. 2.2 Iris Verification Iris recognition is receiving increased attention due to its high reliability [5] The human iris is an annular region between the black pupil and the white sclera. The texture of iris are unique to each subject [5][10] Daugman uses texture of iris image as features for classification[5] The iris is first localized with two circles in the image. Then the iris part is unwrapped to a rectangular region where the iris texture is analyzed. The iris recognition system employed in this paper is based on an ....

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J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. on PAMI, 15(11):1148--1161, 1993.

....researchers, and eye trackers have been commercially available for many years. A comprehensive survey of the earlier works can be found in [16] Our review shall focus on eye tracking based on computer vision techniques. A model based approach to eye image analysis can be seen in Daugman s work [3]. In this application, the machine used for identifying persons needs to seach the input image to localize the iris. The algorithm essentially performs a coarse to fine search for a circular contour in the image corresponding to the limbus, and then searches for the pupil. In this case the model ....

J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148-- 1161, 1993.

....In principle, we can use the false (impostor) acceptance rate (FAR) the false (genuine individual) reject rate (FRR) and the equal error rate (EER) 11 to indicate the identification accuracy of 11 Equal error rate is defined as the value where FAR and FRR are equal. 54 a biometric system [64, 65]. In practice, these performance metrics can only be estimated from empirical data and the estimates of the performance are dependent on the fingerprint database used in the experiments. Therefore, they are meaningful only for a specific database in a specific test environment. For example, the ....

J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993. 64

....are still depending on proper normalization, in particular if only small training sample sizes are used. 4 Recognition of Human Faces Human face recognition is another challenging visual classification task. There exist several reliable methods for identification of persons like iris diagnosis [7] or fingerprint analysis but they are quite uncomfortable so that identification by face images is preferred if the accuracy is sufficient. For face recognition small deviations form a three dimensional shape have to be detected, while the recognizer has to be able to cope with a large variety of ....

J.G. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, pp. 1148-1161, 1993

.... the response of neural cells [Young, 1986] and the existence of a recursive implementation [Deriche, 1987] Furthermore, Gaussian derivatives (as well as Gabor filters) are robust to scale changes of approximately Sigma20 [Schmid and Mohr, 1997] Gabor filters [Gabor, 1946] Westelius, 1992] [Daugman, 1993] satisfy the same constraints as Gaussian filters (robustness, steerability to image plane rotation, equivariance to scale changes) During earlier experiments (not reported below) Gabor filters obtained almost identical results as Gaussian derivatives. Even though color has not been used in our ....

Daugman, J. (1993). High confidence visual recognition of persons by test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161.

....databases containing thousands of people (e.g. 4] However, large intra subject variability casts doubt upon the possibility of scaling face recognition, at least in this form, to very large populations. A form of biometric facial recognition using the iris is better suited to such populations [5]. In contrast, the face recognition tasks considered in this paper are characterised by the availability of many images of relatively small groups of individuals. Such data arise from the type of 2 integrated approach to face recognition in dynamic scenes illustrated in Figure 1. Since these ....

J. G. Daugman, "High confidence visual recognition of persons by a test 16 of statistical independence," IEEE PAMI, vol. 15, no. 11, pp. 1148--1161, November 1993.

....when the user utters a specific word) Insert the previous sotware in the eye tracker to realize a hands free system that allows the user to select icons on the screen (or even move the mouse) without directly accessing keyboard or mouse. Use a simple method such as that presented in Ref. [9] for recognition of user such that the system is user independent after training neural network and storing user specific weights. # Milestones: At least one of the previous improvements should be realized. A real time demonstration is required. ....

J. Daugman, "High confidence visual recognition of persons by a test of statistical independence", IEEE Trans. Patt. Anal. and Mach. Intellig. 15, (11), 1993 3

.... (DFFS) as attractor, ie the squared Euclidian distance to the space spanned by the principal components for the specific face area (eigenmouths, eigeneyes, Together, these areas cover the important area of the face (as discussed earlier) For the eye sites, the iris detector described in [4] is added as well, since it has proven to very precise. Some sites are attracted to edges to find the contours of the face. Both horizontal and vertical edge detections are performed (simply by using Sobel filters) and weighted together at each site according to the direction of the edge. There ....

J.Daugman, "High confidence visual recognition of persons by a test of statistical independence ", IEEE Trans. on PAMI, 15(11):11481161, 1993.

....and the performance of the system are shown. 1 Introduction and Motivation A number of biometric techniques have been proposed for personal identification in the past. Among the vision based ones, we can mention face recognition [21] 22] 23] fingerprint recognition [6] iris scanning [4] and retina scanning. Voice recognition or signature verification are the most widely known among the non vision based ones. Signature verification requires the use of electronic tablets or digitizers for on line capturing and optical scanners for off line conversion [20] These interfaces have ....

J.G. Daugman. High confidence visual recognition of persons by a test of a statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

....University 6 8 Deformable Graphs Figure 6 : Faces located using statistical pattern matching and simulated annealing. to find the optimum . The result is a box marking the probable face area, as shown in Figure 6. 7 Eye Localization To locate the eyes, the algorithm from [38] is used. It works under the assumptions that a) an iris is approximately circular, and b) an iris is dark against a bright background (the white part of the eye) The algorithm, described below, determines in a fast and accurate way how well a part of the image fulfills those assumptions. 7.1 ....

J. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. Pattern Anal. Machine Intell., Vol. 15, No. 11, pp. 1148--1161, 1987.

....there are thus 56440 possible colour block features, of which each image has 340. 3. 3 Texture Features Two dimensional Gabor filters have frequently been proposed as a framework for describing and understanding the orientation and frequency selective properties of neurons in the visual cortex [6], and banks of Gabor filters have often been applied to texture classification and segmentation [14, 39] as well as more general vision tasks [3, 12, 16] We employ a bank of real, circularly symmetric Gabor filters, defined in the spatial domain by fmn (x; y) 1 2 oe 2 m e Gamma x 2 y 2 ....

J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

....right or left eye [4] The system locates the eye in the high resolution image Figure 2. Stereo image and depth map Figure 3. Face template on a subject Figure 4. Template on subject with glasses (figure 5) and extracts a 256 byte iris code , which captures the unique structure of the iris [2] (figure 6) The extracted code is compared to an enrollment iris code stored in a database. This component of the image processing is performed on a standard 166MHz Intel Pentium TM motherboard. This system configuration shows an average verification time of approximately 3 seconds; the only ....

J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148-- 1161, Nov. 1993.

....providing registration facilities for voters and drivers. Currently, there are mainly nine different biometric techniques that are either widely used or under investigation, including face, fingerprint, hand geometry, hand vein, iris, retinal pattern, signature, voice print, and facial thermograms [13, 20, 18, 53, 68]. A brief comparison of these nine biometric techniques is provided in Table 1. Although each of these biometric techniques, to a certain extent, satisfies the above requirements and has been used in practical systems [13, 20, 18, 53] or has the potential to become a valid biometric technique ....

....pattern, signature, voice print, and facial thermograms [13, 20, 18, 53, 68] A brief comparison of these nine biometric techniques is provided in Table 1. Although each of these biometric techniques, to a certain extent, satisfies the above requirements and has been used in practical systems [13, 20, 18, 53] or has the potential to become a valid biometric technique [53] not many of them are acceptable (in court of law) as indisputable evidence of identity. For example, despite the fact that extensive studies have been conducted on automatic face recognition and that a number of face recognition ....

[Article contains additional citation context not shown here]

J. G. Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Trans. on PAMI, Vol. 15, No. 11, pp. 1148-1161, 1993.

....there are thus 56440 possible colour block features, of which each image has 340. 3. 3 Texture Features Two dimensional Gabor filters have frequently been proposed as a framework for describing and understanding the orientation and frequency selective properties of neurons in the visual cortex [6], and banks of Gabor filters have often been applied to texture classification and segmentation [14, 39] as well as more general vision tasks [3, 12, 16] We employ a bank of real, circularly symmetric Gabor filters, defined in the spatial domain by f mn (x; y) 1 2 oe 2 m e Gamma x 2 y ....

J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

.... Sigma20 [Rao and Ballard, 1995] The experiments described throughout the article use Gaussian derivatives because of their robustness as well as because of the steerability to image plane rotation and the equivariance property to scale. Gabor filters [Gabor, 1946, Westelius, 1992, Daugman, 1993] satisfy the same constraints as Gaussian filters (robustness, steerability to image plane rotation, equivariance to scale changes) We used Gabor filters in early experiments. Since the recognition results with Gabor filters and with Gaussian filters are almost identical we do not describe these ....

Daugman, J. (1993). High confidence visual recognition of persons by test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161.

....counterfeited. Currently, there are mainly nine different biometric techniques that are either widely used or under investigation, including face, facial thermograms, fingerprint, hand geometry, hand vein, iris, retinal pattern, signature, and voice print (examples are shown in Figure 1) for [6, 8, 7, 16]. Each of these biometric techniques has its own advantages and disadvantages and is admissible depending on the application. face facial thermograms fingerprint hand vein retinal scan Figure 1. Examples of biometric characteristic. A generic biometric system architecture is depicted in Figure ....

....of the individual s biometric characteristic. Due to intra class variations in the biometric characteristics, the identity can be established only with certain confidence. A decision made by a biometric system is either a genuine individual type of decision or an impostor type of decision [7, 16]. For each type of decision, there are two possible outcomes, true or false. Therefore, there are a total of four possible outcomes: i) a genuine individual is accepted, ii) a genuine individual is rejected, iii) an impostor is rejected, and (iv) an impostor is accepted. Outcomes (i) and (iii) ....

J. G. Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Trans. PAMI, Vol. 15, No. 11, pp. 1148-1161, 1993.

....[7] and face manifold [13] The next section discusses the wavelet transform, which is used to build a multiresolution representation of average detail face characteristics in the first stage of the FuzzyFace system. 4 Wavelet Transform Wavelets have been used previously for face processing [12, 17, 33, 38]. These approaches to feature extraction are based on recent studies of the mammalian visual system, where Gabor wavelets can be used to fit response properties of retinal and simple cortical cells [2, 9, 10, 31, 34, 63] Two dimensional Gabor wavelets have the basic form [12] mpq (x; y) 2 ....

.... [12, 17, 33, 38] These approaches to feature extraction are based on recent studies of the mammalian visual system, where Gabor wavelets can be used to fit response properties of retinal and simple cortical cells [2, 9, 10, 31, 34, 63] Two dimensional Gabor wavelets have the basic form [12]: mpq (x; y) 2 Gamma2m (x 0 ; y 0 ) 1) where dilation by 2 Gammam , translation by p and q, and rotation by of the wavelet is captured in x 0 and y 0 as: x 0 = 2 Gammam [x cos y sin ] Gamma p (2) y 0 = 2 Gammam [ Gammax sin y cos ] Gamma q (3) 4) ....

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J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. PAMI, 15(11):1148--1161, November 1993.

....on either accepting or rejecting the claim. As with any security system, given that the claimant is, or is not, a true instance of the user, there are four possible outcomes; Acceptance of Authentic (AA) Acceptance of Impostor (IA) Rejection of Authentic (RA) and Rejection of Impostor (RI) [Dau93]. Since the first and fourth outcomes are the desired results, one s main goals in designing an authentication system are to maximize the conditional probabilities of (AA) and (RI) while minimizing the likelihoods of (IA) and (RA) In practice, these goals are not always achievable and the level ....

John G. Daugman. High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), November 1993.

....object representations directly from the input images. For example, Buhmann et al. Buhmann et al. 1990 ] use a set of Gabor filters to form composite feature detectors called jets, whose responses to input images are used in an elastic graph matching strategy for recognition. Daugman [ Daugman, 1993 ] uses multiscale 2 D Gabor wavelets to generate long 256 byte iris codes for a human eye which he uses in a scheme for personal identity verification. Viola [ Viola, 1993 ] describes a recognition system that uses the responses of a statistically motivated set of complex local features. Rao ....

John G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11):1148--1161, November 1993.

....four equal sized blocks, at four scales. The occurrence of a block with a given mode color is treated as a binary feature. 3. 2 Texture features Two dimensional Gabor filters (Gabors) have been used to describe the orientation and frequency selective properties of neurons in the visual cortex [11]. Viper employs a bank of real, circularly symmetric Gabors, at three scales and four orientations. The resultant bank of 12 filters gives good coverage of the frequency domain, and little overlap between filters. The mean energy of each filter is quantized into 10 bands, for each of the smallest ....

John G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

.... [32] Filters at Brightness Edge [30] Single Scale Detection [10] Curved Line [56] Grouping [44] Filters at Optic Flow [1] 29] 80] Multiple Scales Shape from Shading [57] 58] Texture Segmentation [39] 45] Stereo Correspondence [37] 34] Scene Interpretation [11] 82] Biometric Signatures [19] Table 2: The trend from variational methods towards the use of filters for solving specific problems in computer vision. 2.4 Iconic Representations from Gaussian Derivative Filters There has been a recent surge of interest in the use of multiscale spatiotemporal filters for proto visual analysis ....

....problem is resolved by an encompassing active vision system which provides the necessary figure ground segmentation. Viola also focuses on object identification and uses an index based on complex local features. Two other schemes employing filterbased vector representations are those of Daugman [19] and Buhmann et al. 11] Daugman used multiscale 2 D Gabor wavelets to generate long 256 byte iris codes for a human eye which he uses in a scheme for personal identity verification; he is however solely concerned with iris recognition rather than recognition of arbitrary objects. Gabor based ....

John G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11):1148--1161, November 1993.

....authentication procedure takes, on an average, about 1.4 seconds on a Sun ULTRA 1 workstation. Keywords: fingerprints, matching, authentication, minutia, orientation field, ridge extraction. 1. Introduction Automatic identity authentication is becoming more and more important in our modern society [2, 3]. A number of automatic identity authentication techniques have been investigated, including blood vessel patterns in the retina or hand, fingerprint, hand geometry, iris, signature, and voiceprints [2] Among them, fingerprint is one of the most reliable techniques [3, 5] In this paper, we will ....

.... Automatic identity authentication is becoming more and more important in our modern society [2, 3] A number of automatic identity authentication techniques have been investigated, including blood vessel patterns in the retina or hand, fingerprint, hand geometry, iris, signature, and voiceprints [2]. Among them, fingerprint is one of the most reliable techniques [3, 5] In this paper, we will introduce an automatic identity authentication system which is capable of authenticating the identity of an individual automatically using his her fingerprints. Such a system has great utility in a ....

J. G. Daugman, High Confidence Visual Recognition of Persons by a Test of Statistical Independence, IEEE Transactions on PAMI, Vol. 15, No. 11, pp. 11481161, 1993.

....of the Gabor filters is that one can freely choose the frequency (and therefore the scale) as well as the bandwidth of the filter. A Gabor filter pair is compact in both space and frequency. In our experiments we have used a two dimensional formulation of the Gabor functions proposed by Daugman [1] (in the Fourier domain) G(u; v) e Gamma ( u Gammau 0 ) 2 ff 2 (v Gammav 0 ) 2 fi 2 ) e Gamma2 i(x 0 (u Gammau 0) y0 (v Gammav 0 ) 2) where (x 0 ; y 0 ) are the center coordinates of the filter, ff; fi) define the width and the length, and (u 0 ; v 0 ) specify the modulation ....

J. G. Daugman. High confidence visual recognition of persons by test of statistical independance. IEEE PAMI, 15(11):1148--1161, November 1993.

....texture analysis, computer vision, image segmentation I. Introduction Gabor filters have been used in many applications, such as texture segmentation [1] 10] target detection [11] 12] fractal dimension measurement [13] document analysis [14] edge detection [15] retina identification [16], image coding [17] 18] and image representation [19] Further, Gabor filters have been shown to possess optimal filter properties and to have similarities to biological vision systems [20] Despite considerable research activity, the design of single or multiple Gabor filters to segment ....

J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. Pattern Anal. Machine Intell., 15(11), 1148--1160 (1993).

....into four equalsized blocks, at four scales. The occurrence of a block with a given mode color is treated as a binary feature. Texture features Two dimensional Gabor filters (Gabors) have been used to describe the orientation and frequency selective properties of neurons in the visual cortex [11]. Viper employs a bank of real, circularly symmetric Gabors, at three scales and four orientations. The resultant bank of 12 filters gives good coverage of the frequency domain, and little overlap between filters. The mean energy of each filter is quantized into 10 bands, for each of the smallest ....

John G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

.... space (DFFS) as attractor, i.e. the Euclidian distance to the space spanned by the principal components for the specific face area (eigenmouths, eigeneyes, Together, these areas cover the important area of the face (as discussed earlier) For the eye sites, the iris detector described in [5] is added as well, since it has proven to very precise. Some sites are attracted to edges to find the contours of the face. Both horizontal and vertical edge detections are performed, and weighted together at each site according to the direction of the edge. There are also a few sites without ....

J.Daugman, "High confidence visual recognition of persons by a test of statistical independence", IEEE Trans. on Patt. Anal. and Mach. Int., 15(11):1148-1161, 1993.

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J. G. Daugman. High confidence visual recognition of persons by a test of statistical indenpendence. In IEEE Patt. Anal. Mach. Intell., volume 15, pages 1148--1161, 1993.

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J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Machine Intel. 15 (1993) 1148--1161. Demodulation by Complex-valued Wavelets 17

....in the near infrared (NIR) wavelengths used for unobtrusive imaging at distances of up to 1 meter, deeper and somewhat more slowly modulated stromal features dominate the iris pattern. In NIR wavelengths, even darkly pigmented irises reveal rich and complex features. Algorithms described in (Daugman 1993, 1994) for encoding and recognizing iris patterns have been the executable software used in all iris recognition systems so far deployed commercially or in public trials, including those by British Telecom, US Sandia Labs, UK National Physical Laboratory, NCR, Oki, IriScan, Iridian, Sensar, and ....

....rotating between any adjacent phase quadrants, only a single bit changes, unlike a binary code in which two bits may change, making some errors arbitrarily more costly than others. Altogether 2,048 such phase bits (256 bytes) are computed for each iris, but in a major improvement over the earlier (Daugman 1993) algorithms, now an equal number of masking bits are also computed to signify whether any iris region is obscured by eyelids, contains any eyelash occlusions, specular reflections, boundary artifacts of hard contact lenses, or poor signal to noise ratio and thus should be ignored in the ....

Daugman, J. 1993. High confidence visual recognition of persons by a test of statistical independence. Trans. Pattern Analysis and Machine Intelligence 15(11): 1148-1161.

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J. Daugman, "High confidence visual recognition of persons by a test of statistical independence, " IEEE Trans. Pattern Analysis and Machine Intelligence Vol. 15, No. 11, 1993, pp. 1148-1161.

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Daugman, J.G., High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence 15(11), 1993, 1148-1161.

....of a bit being set, as shown in Figure 4. If there were any systematic correlations among irises, this plot would not be flat. The fact that it is flat at a value of 0. 5 means that any given bit in an IrisCode is equally likely to be set or cleared, and so IrisCodes are maximum entropy codes (Daugman, 1993) in a bit wise sense. Bit Probabilities Code ....

....p m q (N Gammam) 1.6) where N = 266, p = q = 0:5, and x = m=N is the Hamming Distance. Let F 0 (x) be its cumulative from the left, up to x: F 0 (x) R x 0 f(x)dx. When only the smallest of n samples from such a distribution is kept, the resulting extreme value distribution (derived in Daugman, 1993) has density f n (x) f n (x) nf(x) 1 Gamma F 0 (x) n Gamma1 (1.7) as plotted in Figure 8 (fit to the data will be seen later in Figure 9. The Hamming Distance 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 10 10 10 10 5.5 16 32 57 266 Degrees of Freedom Figure 1.8 Theoretical ....

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Daugman, J.G. (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence 15(11): 1148-1161.

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J.G. Daugman, High confidence visual recognition of persons by a test of statistical significance, IEEE Transactions on Pattern Analysis and Machine Intelligence 15(11) (1993) 1148-1161.

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J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 1148--1161, Nov. 1993.

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J. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Machine Intell., 15(11):1148-1161, November 1993.

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J. G. Daugman, "High confidence visual recognition of persons by a test of statistical independence," IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 1148--1161, Nov. 1993.

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John G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1148--1161, 1993.

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J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Transactions on Pattern Analysis and Machine Intelligence 15 (11) (1993) 1148--1161.

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J.G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. on PAMI, 15(11):1148--1161, 1993.

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J.G. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence", IEEE Trans. PAMI, 15(11): 1148-1161, 1993.

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J.G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. PAMI, 15(11):1148--1161, November 1993.

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J. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.

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J. G. Daugman. High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. and Machine Intell., 15(11):1148--1161, 1993.

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Daugman, J. (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Analysis and Machine Intelligence, 15(11): 1148-1161.

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Daugman J, 1993, "High confidence visual recognition of persons by a test of statistical independence " Trans. on Pattern Analysis and Machine Intelligence, 15, 1148-1161

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J. G. Daugman, High confidence visual recognition of persons by a test of statistical independence, IEEE Trans. Pattern Anal. Machine Intell., 15, 1148--1160, (1993).

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