Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85 (1997) 1365--1388  Home/Search   Document Details and Download   Summary   Related Articles   Check

....for correspondence. scans. Lopez et al. in [9] discusses the use of some discrete multilocal measures for ridge finding. Automatic fingerprint identification systems (AFIS) are a class of biometric techniques widely used for personal identification. They are usually based on minutiae matching [3, 6, 7, 8]. Minutiae, or Galton s characteristics [4] are local discontinuities in terms of terminations and bifurcations of the ridge lines that constitute a fingerprint pattern. These two types of minutiae are considered by Federal Bureau of Investigation for identification purposes [18] AFIS based on ....

....Mehtre described the steps for handling noise in a fingerprint image, enhancement and restoration of the image, and a parallel thinning procedure. A detailed discussion on all the aspects of personal identification using fingerprints as an important biometric technique can be found in Jain et al. [7]. In their method, a segmentation algorithm based on the local certainty level of the estimated orientation field of the fingerprint image is used to locate the region of interest. Ridges in these zones are then detected by convolving the original image with two masks for increasing the local ....

Jain, A. K., Hong, L., Pankanti, S. and Bolle, R., "An Identity-Authentication System Using Fingerprints" , Proc. of IEEE, vol. 85, no. 9, pp. 1365-1388, Sep., 1997.

....Minutiae are used for fingerprint matching, which is a one to one comparison of two fingerprints. One of the first steps in fingerprint recognition is the extraction of the minutiae from the fingerprint image. The classical approach uses a number of image processing steps for this task [3]. First, the fingerprint image is filtered for noise suppression. Then, a threshold is applied to the image in order to obtain a binary image. Next, the ridges are thinned to 1 pixel width by morphological operations. From this skeleton, minutiae extraction is a straightforward task. However, this ....

A.K. Jain, L. Hong, S. Pankanti, and R. Bolle. An identity-authentication system using fingerprints. Proc. of the IEEE, 85(9):1365--1388, September 1997.

....approach, the system was tested with the entire Nist 4 database fingerprint. The achieved verification rate on the entire NIST 4 database is above 92 . 1 Introduction Fingerprints are characterized by a system of lines having a certain bending, by terminal points and by bifurcation of lines [8]. A lot of noise problems can arise with ink on paper fingerprint images. Traditionally, the fingerprint images are acquired with a scanner giving images with ink stains, translation and rotation problems. The above hindrances make very hard the fingerprint verification task and very robust ....

....dimension are necessary to eliminate the impurity islands of different dimension In the fourth subtask a thinning algorithm is applied in order to obtain well defined and distinguished ridges [2] 4] 2. 2 Minutiae extraction phase This phase deals with fingerprint characteristics extraction [8], 9] 10] The extracted characteristics are the minutiae (endpoint or bifurcation) For each image, if n is the number of the extracted minutiae, a set of n records r(X, Y, Q) is obtained, where X and Y are the spatial coordinates of the minutia and Q is the angle between the minutia direction ....

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K. Jain, L. Hong, S. Pankanti, R. Bolle, An identity-Authentication system using fingerprints, Proceeding of IEEE, Vol.85, No.9, 1997, pp.1365-1388.

....12 minutia in a 500 dpi optical fingerprint scan. The entry (35, 12, 12, 12) corresponds to the 12 point rule. 83 2. 6 Fingerprint correspondence probabilities obtained from matching imposter fingerprints using an AFMS [11] for the MSU VERIDICOM and MSU DBI databases. The probabilities given in the table are for matching exactly q minutiae. The probabilities for matching q or more minutiae are 3.010 2 and 3.210 2 for the MSU VERIDICOM and MSU DBI databases, respectively, i.e. of the same order. The ....

..... 34 2.3 Minutiae extraction for twins. a) and (b) are fingerprint images of an identical twin and his her sibling while the fingerprint in (c) is from another person. d) e) and (f) are the minutiae extracted from (a) b) and (c) respectively using the extraction algorithm in [11]. 36 xiii 2.4 Minutiae matching for (a) twin nontwin (matching of Figures 2.3(e) and 2.3(f) matching score = 3 on a scale of 0 999) and (b) twin twin (matching of Figures 2.3(d) and Figure 2.3(e) matching score = 38 on a scale of 0 999) The matched minutiae pairs are shown by ....

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A. K. Jain, L. Hong, S. Pankanti, and Ruud Bolle, "An Identity Authentication System Using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 13651388, 1997.

....pairs of aligned event. The string matching technique presented here is a simpli cation of a technique that has been previously applied to ngerprint recog39 (a) b) Figure 3.3: Examples of characters (a) before and (b) after preprocessing with critical points marked as dots. 40 nition [47]. Similar methods under the name elastic template matching have been applied to online handwritten data [103] a) Distance=108 (b) Distance=10 Figure 3.4: The alignment between two digits, and the resulting matching scores for (a) a 2 compared with a 3 , b) comparing two 3 s. Dotted lines ....

A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, \An Identity-Authentication System Using Fingerprints," Proc. of the IEEE, vol. 85, no. 9, pp. 1365-1388, Sept. 1997.

....Several examples 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 ....

A.K. Jain, L. Hong, S. Pankanti, and E. Bolle. An identity-authentication system using fingerprints. Proceedings of the IEEE, 85(9):1365--1388, 1997.

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85 (1997) 1365--1388

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A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity-Authentication System Using Fingerprints," Proc. IEEE, vol. 85, no. 9, pp. 1365-1388, Sept. 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proc. IEEE, vol. 85, pp. 1365--1388, Sept. 1997.

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85 (1997) 1365--1388

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A. Jain, L. Hong, S. Pankanti, and R. Bolle. An identity-authentication system using fingerprints. Proceedings of IEEE, 85(9):1365--1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 13651388, 1997.

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A.K. Jain, L. Hong, S. Pankanti and R. Bolle, \An Identity Authentication System Using Fingerprints", Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, \An Identity Authentication System using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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A. K. Jain, Lin Hong, S. Pankanti, and Ruud Bolle, "An identity authentication system using fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997. 3

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A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints", Proc. IEEE, vol. 85, no. 9, Sept. 1997, pp. 1365-1388.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle. An identity-authentication system using fingerprints. Proceedings IEEE, 85(9):1365--1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 13651388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using finger- prints," Proceedings of the IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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# A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proc. IEEE, vol. 85, no. 9, pp. 1,365-1,388, 1997.

....The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae based algorithms published in the open literature [1]. Our system performs better than a state of the art minutiae based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on ....

....different fingers. The popular fingerprint representation schemes have evolved from an intuitive system design tailored for fingerprint experts who visually match the fingerprints. These schemes are either based on predominantly local landmarks (e.g. minutiae based fingerprint matching systems [1], 5] or exclusively global information (fingerprint classification based on the Henry system [6] 8] The minutiae based automatic identification techniques first locate the minutiae points and then match their relative placement in a given finger and the stored template [1] A good quality ....

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proc. IEEE, vol. 85, pp. 1365--1388, Sept. 1997.

.... (e.g. fingerprints, face, iris) that are unique to an individual and hence can be used for personal authentication [6] Unlike token based authentication (e.g. ID cards or keys) and knowledge based authentication (e.g. passwords and PINs) biometrics data cannot be easily forged or guessed [7] [8]. Assuming that the biometrics system is secure (for a thorough treatment of possible attacks on a biometrics system, see [9] adding the biometric data of the user into the encryption decryption process can increase the security of the digital content. For example, in the scenario mentioned ....

A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identityauthentication system using fingerprints", Proc. IEEE, vol. 85, no. 9, pp. 1365-1388, Sept. 1997.

....as minutiae points (ridge endings and ridge bifurcations) that are present in every fingerprint (Figure 5) The matching process involves comparing the two dimensional minutiae patterns extracted from the user s print with those in the template. For this experiment, the techniques described in [25] were used in the feature extraction and matching stages of the fingerprint verification system. 4.3 Hand Geometry Hand geometry, as the name suggests, refers to the geometric structure of the hand. This structure includes width of the fingers at various locations, width of the palm, ....

A. K. Jain, L. Hong, S. Pankanti, R. Bolle, An identity authentication system using fingerprints, Proceedings of the IEEE 85 (9) (1997) 1365--1388.

....fingerprint matching domain. Most of the existing automatic fingerprint verification systems are based on minutiae features (ridge bifurcation and ending; see Figure 2) Such systems first detect the minutiae in a fingerprint image and then match the input minutiae set with the stored template [1, 2]. Several methods have been proposed for minutiae extraction that involve binarizing and thinning steps [3, 4, 5] An algorithm described in [1] is a typical example of a sequential approach to feature extraction (see Figure 3) The feature extraction module first binarizes the ridges in a ....

.... and ending; see Figure 2) Such systems first detect the minutiae in a fingerprint image and then match the input minutiae set with the stored template [1, 2] Several methods have been proposed for minutiae extraction that involve binarizing and thinning steps [3, 4, 5] An algorithm described in [1] is a typical example of a sequential approach to feature extraction (see Figure 3) The feature extraction module first binarizes the ridges in a fingerprint image using masks that are capable of adaptively accentuating the local maximum grayscale values along a direction normal to the local ....

[Article contains additional citation context not shown here]

A. K. Jain, L. Hong, S. Pankanti, R. Bolle, An identity authentication system using fingerprints, Proceedings of the IEEE, 85(9)(1997) 1365-1388.

.... as the watermark face image in Figure 2(c) In order to assess the effect of watermarking on fingerprint verification accuracy, the reconstructed fingerprint images shown in Figures 2(f) and 2(i) and the original image shown in Figure 2(a) are compared using the fingerprint matcher described in [3]. The matcher gives a score of 81 and 72, respectively, out of a maximum possible value of 100. ROC curves for original images and images that are recovered after watermark decoding are also computed. A total of 640 fingerprint images are used in our experiments. These images come from 160 users, ....

A.K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity-authentication system using fingerprints", Proc. IEEE, vol. 85, no. 9, Sept. 1997, pp. 1365-1388.

....improvements in order to adapt this technique for matching images captured by solid state sensors: i) Estimate the translation and rotation parameters needed to align the input image with the template using their minutiae points. ii) Use the foreground segmentation algorithm described in [6] to segment the input and the template images. iii) Define a rectangular tessellation on the two aligned images. Extract texture features from each rectangular cell (of both images) using Gabor filters. iv) Match features extracted from all the overlapping foreground cells and weigh the matching ....

....the hybrid technique because it combines minutiae information available in a fingerprint with the underlying texture information in local regions to perform the matching. 3.1. Image Alignment Minutiae points from both the input and template images are extracted using the algorithm described in [6]. The algorithm provides the following two outputs: a) A set of minutiae points, each characterized by its spatial position and orientation in the fingerprint image. b) Local ridge information in the vicinity of each minutiae point. The two sets of minutiae points are then matched using a point ....

[Article contains additional citation context not shown here]

Anil K. Jain, Lin Hong, Sharat Pankanti, and Ruud Bolle, "An identity authentication system using fingerprints, " Proceedings of the IEEE, vol. 85, no. 9, pp. 1365--1388, 1997.

....provided by each user were used in the training phase, and the rest were used in the test phase. Fingerprint images were acquired using a Digital Biometrics sensor (500 dpi, 640 480) and an Identix sensor (500 dpi, 255 256) Minutiae features were used to represent and match fingerprints [9]. Images of a subject s face were obtained using a Panasonic CCD (640 480) and a Sony video camera (256 384) The eigenface approach was used to extract features from the face image [10] Face matching involved computing the Euclidean distance between the coefficients of the eigenface for ....

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proceedings of the IEEE, vol. 85, no. 9, pp. 1365-- 1388, 1997.

....of 500 dpi. The features correspond to the position and orientation of certain critical points, known as minutiae, that are present in every fingerprint. The matching process involves comparing the two dimensional minutiae patterns extracted from the user s print with those in the template [11]. 3. Hand Geometry Verification: Images of a subject s right hand were captured using a Pulnix TMC 7EX camera. The feature extraction system computes 14 feature values comprising of the lengths of the fingers, widths of the fingers and widths of the palm at various locations of the hand [10] ....

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," in Proceedings of the IEEE, vol. 85, pp. 1365-1388, 1997.

....called ridge endings and ridge bifurcations, are identified in feature extraction stage. During matching, a similarity value between the features extracted from the template and the input fingerprint images is calculated. This similarity value is used to arrive at an accept reject decision [4] [5]. While biometrics techniques have inherent advantages over traditional personal identification techniques, the problem of ensuring the security and integrity of the biometrics data is critical. For example, if a person s biometric data (e.g. his her fingerprint image) is stolen, it is not ....

....and the attributes ) # y x of the extracted minutiae. These attributes constitute the data to be hidden in the host images. The fingerprint images (300x300) used here were captured by a solid state sensor manufactured by Veridicom. The minutiae are extracted using the method outlined in [5]. The minutiae data shown in Figure 3(c) contain three fields per minutiae: xcoordinate, y coordinate and orientation, for a total of 25 minutiae. The minutiae data ( 25 . 2 , 1 ) i y x i i i # ) are hidden in the cover image using the method explained in Section 3.2. A secret ....

A.K. Jain, L. Hong, S. Pankanti and R. Bolle, "An identityauthentication system using fingerprints", Proc. IEEE, vol. 85, no. 9, Sept. 1997, pp. 1365-1388.

....fingerprint matching domain. Most of the existing automatic fingerprint verification systems are based on minutiae features (ridge bifurcation and ending; see Figure 2) Such systems first detect the minutiae in a fingerprint image and then match the input minutiae set with the stored template [1, 2]. Several methods have been proposed for minutiae extraction which involve binarizing and thinning steps [3, 4, 5] An algorithm described in [1] is a typical example of a sequential approach to feature extraction (see Figure 3) The feature extraction module first binarizes the ridges in a ....

.... ending; see Figure 2) Such systems first detect the minutiae in a fingerprint image and then match the input minutiae set with the stored template [1, 2] Several methods have been proposed for minutiae extraction which involve binarizing and thinning steps [3, 4, 5] An algorithm described in [1] is a typical example of a sequential approach to feature extraction (see Figure 3) The feature extraction module first binarizes the ridges in a fingerprint image using masks that are capable of adaptively accentuating the local maximum grayscale values along a direction normal to the local ....

[Article contains additional citation context not shown here]

A. K. Jain, L. Hong, S. Pankanti, R. Bolle, An identity authentication system using fingerprints, Proceedings of the IEEE, 85(9)(1997) 1365-1388.

....upper bound on the performance of automatic fingerprint verification systems. Here, we develop a fingerprint individuality model that attempts to estimate the probability of a false association. We use this model to establish an upper bound on the performance of a fingerprint verification system [4]. In order to solve the individuality problem, we need to first define a priori the representation of fingerprint (pattern) and the metric for the similarity. Fingerprints can be represented by a large number of features, including the overall ridge flow pattern, ridge frequency, location and ....

.... to the discriminability of the di#erent patterns (e.g. minutiae from di#erent fingers) The e#ective estimation of discriminatory information can only be achieved by taking into account intra pattern variations [33] There are several sources of variability in the multiple impressions of a finger [4]: non uniform contact (with the sensor) irreproducible contact, inconsistent contact, and imaging artifacts. This variability in multiple impressions of a finger manifests itself into (i) detection of spurious minutiae or missing genuine minutiae, ii) displacement disorientation (also called ....

[Article contains additional citation context not shown here]

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

....probability. 3. Fingerprint Individuality Model We have developed a model to obtain a realistic and accurate probability of correspondence between fingerprints. The probabilities obtained using this model will be compared against empirical values using an automatic fingerprint matching system [4]. To estimate the probability of correspondence, we make the following assumptions: i) We consider only two types of minutiae features: ridge endings and ridge bifurcations. Additionally, we do not distinguish between the two types of minutiae because they can not be accurately discriminated. ....

....collected fingerprint images using a solid state fingerprint sensor manufactured by Veridicom, Inc. image size = 300 300, resolution = 500 dpi) We call this database VERIDICOM. A large number of impostor matchings (over 4, 000, 000) were generated using an automatic fingerprint matching system [4]. 0 5 10 15 20 0 0.05 0.1 0.15 0.2 0.25 Number of matching minutiae (q) Probability Empirical Theoretical Figure 4. Comparison of experimental and theoretical probabilities for the number of matching minutiae in impostor fingerprint matches for the DBI database. The mean values of ....

[Article contains additional citation context not shown here]

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An Identity Authentication System Using Fingerprints," Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

....(1D) string by concatenating points in a increasing order of radial angel in polar coordinate. The string matching algorithm is applied to compute the edit distance between the two strings. The edit distance can be easily normalized and converted into a matching score. This algorithm [9] can be summarized as follows: 1) Rotation and translation are estimated by matching ridge segment (represented as planar curve) associated with each minutia in the input image with the ridge segment associated with each minutia in the template image. The rotation and translation that results in ....

A.K. Jain, L. Hong, S. Pankanti and R. Bolle, \An Identity Authentication System Using Fingerprints", Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

....pairs of identical twins were scanned at 500 dpi resolution. Due to di erences in paper quality and degradation of the print over time, several of these ngerprints are of poor quality. As a result, we used only 94 pairs of identical twin ngerprints in our study. Using the algorithm described in [8], we matched every ngerprint in our twin database with every other ngerprint. The twin twin imposter distribution was estimated using 188 (94 2) matchings between the 94 twin ngerprint pairs whereas the twin nontwin imposter distribution was estimated using 17; 484 (94 93 2) matchings. The ....

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, \An Identity Authentication System Using Fingerprints," Proc. IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

....between the individual pairs of aligned events. This string matching technique is used to provide a distance measure between character pairs. Similar methods under the name elastic template matching have been applied to online handwritten data [10] In our approach, adopted from Jain et al. [23], each event is represented with 3 measurements: the x and y o sets with respect to some reference coordinate, and the angle of curvature of the written stroke at the sample point. We de ne the reference coordinate as the 11 (0; 0; 223) 2; 1; 223) 1; 2; 223) 0; 5; 182) 1; 9; 194) 3; ....

A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, \An Identity-Authentication System Using Fingerprints," Proceedings of the IEEE, vol. 85, no. 9, pp. 1365-1388, Sept. 1997.

....improvements in order to adapt this technique for matching images captured by solid state sensors: i) Estimate the translation and rotation parameters needed to align the input image with the template using their minutiae points. ii) Use the foreground segmentation algorithm described in [6] to segment the input and the template images. iii) Define a rectangular tessellation on the two aligned images. Extract texture features from each rectangular cell (of both images) using Gabor filters. iv) Match features extracted from all the overlapping foreground cells and weigh the matching ....

....the hybrid technique because it combines minutiae information available in a fingerprint with the underlying texture information in local regions to perform the matching. 3.1. Image Alignment Minutiae points from both the input and template images are extracted using the algorithm described in [6]. The algorithm provides the following two outputs: a) A set of minutiae points, each characterized by its spatial position and orientation in the fingerprint image. b) Local ridge information in the vicinity of each minutiae point. The two sets of minutiae points are then matched using a point ....

[Article contains additional citation context not shown here]

Anil K. Jain, Lin Hong, Sharat Pankanti, and Ruud Bolle, "An identity authentication system using fingerprints," in Proceedings of the IEEE, 1997, vol. 85, pp. 1365--1388.

....the events between the two strings, and calculating string distances based on that alignment and the distances between the individual pairs of aligned event. The string matching technique presented here is a simplification of a technique that has been previously applied to fingerprint recognition [7]. Similar methods under the name elastic template matching have been applied to dynamic handwritten data [15] 13 15 10 5 10 15 10 6 4 2 8 10 70 60 50 40 30 20 10 10 20 30 20 15 10 5 10 15 20 25 30 25 20 15 10 5 10 15 20 20 15 10 10 15 Y X (a) 40 ....

Jain, A.K., L. Hong, S. Pankanti, and R. Bolle, "An IdentityAuthentication System Using Fingerprints," Proceedings of the IEEE, Vol. 85, No. 9, Sept. 1997, pp. 1365-1388.

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85 (1997) 1365--1388

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A. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proceedings of the IEEE, vol. 85, no. 9, September 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle. An identity authentication system using fingerprints. Proceedings of the IEEE, 85(9):1365--1388, 1997.

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proceedings of the IEEE 85(9), pp. 1365--1388, 1997.

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE 85 (1997) 1365--1388

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A. K. Jain, L. Hong, S. Pankanti, and R. Bolle, "An identity authentication system using fingerprints," Proceedings of the IEEE 85(9), pp. 1365--1388, 1997.

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity authentication system using fingerprints. Proceedings of the IEEE, Vol. 85, No. 9, September 1997, 1365-1388

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Jain, A.K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. Proceedings of the IEEE, vol. 85, no. 9, Sept. 1997, pp. 1365-1388.

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A. Jain, L. Hong, S. Pankanti and R. Bolle.. An Identity-Authentication System Using Fingerprints. Proc. of the IEEE, volume 85, number 9, 1997.

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Jain, K., Hong, L., Pankanti, S., Bolle, R.: An identity-authentication system using fingerprints. In: Proc. of IEEE. Volume 85. (1997) 1365--1388

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A. Jain, L. Hong, S. Pankanti, R. Bolle. An Identity Authentication System Using Fingerprints. Proceedings of the IEEE, Vol. 85, No. 9, pp. 1365-1388, 1997.

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