Patent Publication Number: US-2009228968-A1

Title: Authentication With Variable Biometric Templates

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to the co-pending U.S. Provisional Application Ser. No. 60/291,900, filed May 18, 2001, entitled “Network-Based Biometric Authentication,” the entirety of which is incorporated herein by reference. 
    
    
     FIELD OF INVENTION 
     The invention relates generally to biometrics. More specifically, in one embodiment, the invention relates to systems and methods for using biometric authentication over a network. 
     BACKGROUND 
     The Internet accords a global community of computer users access to applications and information that traditionally were highly restricted. For example, users can now undertake a wide variety of financial transactions online, or obtain access to financial and other sensitive records online. The increased accessibility of such information, while enormously convenient, jeopardizes privacy and invites tampering and electronic theft. In some known prior art systems, sensitive information that was once physically guarded can now be obtained on the Internet by anyone who can generate the correct server URL, logon and password. 
     Indeed, the mere need for Internet users to keep track of multiple URLs, logon names, passwords and PINs in order to access different information further increases the chances of unauthorized use and loss of private information. Users may resort to using the same logon name and password combinations for all accounts, rendering them equally vulnerable if unauthorized access to a single account is obtained. On the other hand, security-conscious users who maintain different logon names and passwords for individual accounts may, to avoid confusion, write them down where they may be found or store them on easily stolen devices such as personal digital assistants-thereby undermining their own efforts. It can be argued that those who routinely change their passwords but record them on paper or in a computer file are at greater risk of being compromised than those who use a single but difficult-to-crack password. At the very least, such security-conscious individuals risk forgetting their access information, necessitating time-consuming calls to customer-support lines. 
     From the perspective of authentication, passwords and PINs cannot guarantee identity; the identification is no more reliable than the security of the password. In some known prior art systems with password authentication, the server carrying out a transaction can only prove that the correct password was entered-not that it was entered by an authorized person. A password can originate from password-cracking software just as easily as from the real user. Digital certificates improve security by authenticating an end point (i.e., that a message originated with a particular client terminal), but cannot create a non-repudiated link to support the claim that a particular user really did engage in a transaction. 
     SUMMARY OF THE INVENTION 
     The present invention utilizes biometric indicia to offer highly reliable authentication that creates links that cannot be repudiated for transactions initiated within the context of an authenticated session. Unlike passwords, which are no more than secrets vulnerable to theft, biometrics validation matches physical characteristics of the user against stored characteristics to identify the user. Once a user is positively identified, in one embodiment, the server unlocks and validates the user&#39;s credentials for presentation to other servers that request such authentication. A user&#39;s credentials may, for example, represent an account login/password combination or X.509 certificate. This biometric approach offers substantial flexibility in terms of accessibility (from computers, mobile devices, etc.) and relieves the user from responsibility for managing the integrity of such credentials. Biometric scanners are inexpensive and small, and may, for example, be easily incorporated into keyboards and mobile client devices. 
     In one embodiment, the authentication process can use an adaptive learning algorithm to improve the accuracy and reliability of matching a candidate set of biometric data against a user&#39;s biometrics profile (e.g., a reference set of biometric data stored as, for example, a template). Candidate sets of biometric data that result in successful matches are used to augment the profile and improve the statistics need to establish a subsequent reliable match. Upon authentication, new biometric data are introduced into the reference set associated with the profile if it is dissimilar or covers different portions of the biometrics (e.g., different areas of a finger) than other biometric data in the profile (e.g., template). The end result of this process is a gradual tuning of the matching process to the peculiarities exhibited by a user, thereby enhancing accuracy, speed and flexibility. This adaptation also accommodates the gradual changes in a subscriber&#39;s biometric data (e.g., fingerprints) over time. 
     In another embodiment, the authentication process uses a challenge-response protocol. Using of the challenge-response protocol, neither the server nor the client transmit a full set of biometric data across the network during the authentication session. The server makes a copy of the user&#39;s biometric data and modifies the copy to generate a challenge template. The modifying can include eliminating some of the geometric data representing the biometric features (e.g., only including the x, y coordinates of a feature) and inserting fictitious data (e.g., random noise). The server transmits the challenge template to the client. The client receives the challenge template and compares the challenge template to a candidate set of biometric data. Based on the comparison, the client generates a response vector. The response vector can be, for example, a hash code. The client transmits the response vector back to the server. The response vector indicates the portions of the challenge template that did not match the candidate set of biometric data. The server, knowing what fictitious data was inserted into the challenge template can determine if the mismatches sufficiently match the fictitious data. If they do, the server can authenticate the user. 
     In one aspect, the invention relates to a method for authentication using biometrics. The method comprises receiving a request for authentication of a user and receiving a first set of biometric data from the user. The method also comprises comparing the first set of biometric data with a second set of biometric data in storage; and modifying the second set of biometric data in storage based at least in part on the first set of biometric data, if the second set of biometric data sufficiently matches the first set of biometric data. In one embodiment, the method further includes replacing the second set of biometric data in storage with the first set of biometric data, if the second set of biometric data sufficiently matches the first set of biometric data. 
     In another embodiment, the method further includes identifying one or more features in the first set of biometric data, matching at least a portion of the one or more features in the second set of biometric data and augmenting the second set of biometric data with the first set of biometric data based at least in part on the matched features. In another embodiment, the method further includes augmenting the second set of biometric data with features from the first set of biometric data not presently included in the second set of biometric data. In still another embodiment, the method further includes augmenting statistical data associated with the second set of biometric data based at least in part on the first set of biometric data. In yet another embodiment, the method further includes augmenting statistical by increasing the weighting of the matched features. 
     In another embodiment, the method further includes normalizing and/or filtering the first set of biometric data. In another embodiment, the method further includes extracting from the first set of biometric data identifying features. The second set of biometric data may be stored in a supertemplate. 
     In another aspect, the invention relates to a system for authentication using biometrics. The system preferably comprises a network interface, a storage module, an authentication module and an augmentor module. The network interface module may be configured to receive an authentication request requesting authentication of a user and to receive a first set of biometric data from the user. The storage module may be configured to store a second set of biometric data. In general, the authentication module is configured to compare the first set of biometric data with the second set of biometric data in storage. The augmentor module may modify the second set of biometric data in storage based at least in part on the first set of biometric data if the second set of biometric data sufficiently matches the first set of biometric data. 
     In one embodiment, the augmentor module is further configured to replace the second set of biometric data in storage with the first set of biometric data if the second set of biometric data sufficiently matches the first set of biometric data. In another embodiment, the augmentor module is further configured i) to identify one or more features in the first set of biometric data, ii) to match at least a portion of the one or more features in the second set of biometric data and iii) to augment the second set of biometric data with the first set of biometric data based at least in part on the matched features. In still another embodiment, the augmentor module is further configured to augment the second set of biometric data with features from the first set of biometric data not presently included in the second set of biometric data. 
     In another embodiment, the augmentor module is further configured to augment statistical data associated with the second set of biometric data based at least in part on the first set of biometric data. For example, the augmentor module may be configured to increase the weighting of the matched features. In another embodiment, the system further comprises a normalizer module configured to normalize the first set of biometric data. In still another embodiment, the system further comprises a filter module configured to filter the first set of biometric data. In yet another embodiment, the system further comprises an extractor module configured to extract identifying features from the first set of biometric data. The second set of biometric data may be stored in a supertemplate. 
     In another aspect, the invention relates to an article of manufacture having computer-readable program portions embodied therein for authentication using biometrics. The article comprises computer-readable program portions for performing the method steps as described above. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS  
       The above and further advantages of the invention may be better understood by referring to the following description taken in conjunction with the accompanying drawing, in which: 
         FIG. 1  is block diagrams of illustrative embodiments of a system to authenticate a user using augmented biometric data accordance with the invention; 
         FIG. 2  is a block diagram of an illustrative embodiment of a supertemplate used to authenticate a user in accordance with the invention; 
         FIG. 3  is a block diagram of another illustrative embodiment of a system to authenticate a user using augmented biometrics in accordance with the invention; 
         FIG. 4  is a block diagram of another illustrative embodiment of a supertemplate used to authenticate a user in accordance with the invention; and 
         FIG. 5  is a flow diagram of an illustrative embodiment of a process to authenticate a user using augmented biometrics in accordance with the invention. 
     
    
    
     DETAILED DESCRIPTION  
     In broad overview,  FIG. 1  illustrates an embodiment of a system  100  to authenticate a user using augmented biometric data in accordance with the invention. The system  100  includes a first computing system (“a server node”)  108  and a second computing system (“a client node”)  112 , all in communication with a network  116 . The server node  108  and the client node  112  are in communication with the network using communication channels  120 . 
     For example, the network  116  and the communication channels  120  can be part of a local-area network (LAN), such as a company Intranet, a wide area network (WAN) such as the Internet or the World Wide Web or the like. The nodes  108  and  112  communicate with the network  116  through the communication channels  120  using any of a variety of connections including, for example, standard telephone lines, LAN or WAN links (e.g., T1, T3, 56 kb, X.25), broadband connections (ISDN, Frame Relay, ATM), wireless connections and the like. The connections can be established using a variety of communication protocols (e.g., HTTP(S), TCP/IP, SSL, IPX, SPX, NetBIOS, Ethernet, RS232, direct asynchronous connections, a proprietary protocol and the like). In one embodiment, the server  108  and the client  112  encrypt all communication when communicating with each other. 
     The server node  108  can be any computing device capable of providing the services requested by the client node  112 . Particularly, this includes authenticating a user at the client node  112  using biometric data, as described in more detail below. The server node  108  may include a network interface module  124 , an authentication module  128 , an augmentor module  132  and a storage module  135 . The storage module  135  (which may be, for example, persistent memory, one or more hard disks, optical drives and the like) can include a template  136 , in which a reference set of biometric data is stored. The server node  108  can also include one or more optional modules that add additional features for the collection of biometric data and are used in path  138 , i.e., between the network interface module  124  and the authentication module  128 . For example, the server  108  can include a normalizer module  142 , a filter module  144  and/or an extractor module  146 . The modules discussed throughout the specification are implemented as a software program and/or a hardware device (e.g., ASIC, FPGA, processor, memory, storage and the like). In one embodiment, one or more of the optional modules  142 ,  144  and/or  146  may be included on the client  112  instead of or in addition to the server  108 . Placing the one or more of the optional modules  142 ,  144  and/or  146  on the client  112  distributes the processing task and lowers needed bandwidth on the network  116 . 
     For clarity,  FIG. 1  depicts server node  108  as a single server. It is to be understood, however, that the server node  108  can also be implemented, for example, distributed on portions of several (i.e., more than two) servers. The client node  112  can be any computing device (e.g., a personal computer, set top box, wireless mobile phone, handheld device, personal digital assistant, kiosk, etc) used to provide a user interface to access the server  108 . The client node  112  receives biometric data from a biometric input device  160  (e.g., a fingerprint scanner, a retina scanner, a thermal imager, a skin spectrometer, a voice print analyzer, a digital camera and the like). 
     To use the system  100 , a user  170 , also referred to as a subscriber, registers that user&#39;s  170  biometric data with the system  100 . In the illustrated embodiment, the client  112  receives biometric data from the biometric input device  160 . The biometric data can include, for example, data associated with the individual&#39;s fingerprint(s), facial characteristics, voice and the like. The system  100  stores a set of biometric data associated with the user  170  in the storage module  135 . In one embodiment, the biometric data is stored using an alias (e.g., a unique identifier with no personal or other type of information that can identify an individual), so that if the security of the storage module  135  is compromised, the biometric data cannot be associated with a particular individual. 
     In general overview, when the user  170  requests a service over the network  116  that requires authentication, the client device  112  receives a candidate set of biometric data from the biometric input device  160  and transmits it to the server node  108 . The network interface module  124  receives the candidate set of biometric data and transmits it to the authentication module  128 . The authentication module  128  retrieves a reference set of biometric data associated with the user  170  from the storage module  135 . If the candidate set of biometric data sufficiently matches the reference set of biometric data, the authentication module  128  authenticates the user as the registered individual. 
     To authenticate, the authentication module  128  and/or the optional modules  142 ,  144  and/or  146  process the received candidate set of biometric data to extract the unique features that distinguish one set of biometric data (e.g., fingerprint) from another. For example, the normalizer module  142  normalizes the biometric data into a format used by the authentication module  128  and stored in the storage module  135 . The normalization can include, for example, a translation algorithm, a transformation algorithm and the like. The normalization allows the biometrics data to be converted into a standard image suitable for subsequent processing and preferably includes geometric processing to adjust for size differences between sensors, orientation adjustments to invert or rotate images, density adjustments to correct for number of gray levels/dynamic range and sampling adjustments to account for different sensor resolutions. This allows the client device  112  to interface with different types of biometric input devices  160  (e.g., fingerprint readers produced by different manufacturers and having diverse capture resolutions or characteristics) without the need to re-register the user  170  or change the format of the biometric data in the storage module  135 . 
     The filter module  144  filters the received candidate set of biometric data. The filtering can include standard filtering algorithms for correcting blurring of the image, for removing random noise in the image and the like. For example, all captured scans can be checked for partial or blurred prints that exhibit greater than expected amount of change between consecutive frames as well as contrast. Images that exhibit excessive blur can be rejected. Contrast issues can be resolved by asking the user to press down to make better contact with the sensor. Image processing software may be used to enhance the quality of the image and involve signal averaging, noise filtering, ridge/valley enhancement as well as gray scale equalization. The filtering can also include filtering algorithms needed because of the type of the biometric device  160  or the type of user features the biometric device  160  uses. The filtering can also include filtering algorithms based on the type of image (e.g., grainy, wet, fine grain and the like), the finger type and/or personal biometric characteristics (e.g., sex, age and the like). In an embodiment where the filter module  144  is implemented on the client  112 , the filter module  114  operates in conjunction with the biometric input device  116  to perform blur removal, finger detection and time based enhancements. For example, two or more scans may be taken to ensure the user  170  has placed a stable finger (not moving) on the sensor. A difference is then taken between subsequent scans to ensure consistency between the two scans. With noisy sensors, the filter module  144  may integrate consecutive images to reduce the noise level in the captured image. 
     The extractor module  146  extracts the geometric data representing biometric features and/or minutiae from the candidate set of biometric data. In an embodiment where the extractor module  146  is implemented on the client  112 , the extractor module  146  transmits the results to the authentication module  128  using the network  116 . Biometric data, for example in the case of fingerprints, can be divided into global features that are spatial in nature and local features that represent details captured in specific locations. The geometric data can include, for example in the case of fingerprints, the locations (e.g., x, y coordinates) of the features, the type of feature (e.g., ridge ending, bifurcation and the like), the angular data of the features, the slope of the ridge, the neighborhood ridge counts and/or the like. Once the geometric data is processed, the authentication module  128  compares the data of the reference set of biometric data stored in the storage module  135  with the candidate set of biometric data to produce a goodness of fit or confidence of match by examining the local features on a minutia by minutia basis. To calculate the goodness of fit, the authentication module  128  determines the best spatial alignment between the location of minutiae points within the reference set of biometric data and corresponding minutiae points within the candidate set of biometric data. Determining the best spatial alignment involves, for example, finding the rotation angle that produces the greatest number of matching points. Matching can be a relative term, meaning the points are close to each other within some predefined distance. The determining process preferably accommodates both spatial and rotational displacement between the reference set of biometric data and the candidate set of biometric data. This may be accomplished, for example, using a spatial correlation algorithm in which the features of the candidate set of biometric data are translated and rotated about a test alignment point and then compared against the features in the reference set. Different alignment points and rotation angles are tested to determine the lowest difference between the candidate and reference feature set. Once the differences between the local features at each of the matching minutiae points are minimized, the authentication module  128  sums the goodness of fit. 
     The authentication module  128  determines the sufficiency of the match by statistically analyzing the goodness of fit for local features at each of the matching minutiae points and determining whether the probability that they come from the same individual is above a certain predetermined threshold. In one embodiment, an administrator of the system  100  sets the predetermined threshold. The predetermined threshold determines both the false acceptance rate (i.e., the probability that the authentication module  128  will incorrectly authenticate a user) and the false rejection rate (i.e., the probability that the authentication module  128  will incorrectly reject the user when that user is in fact the registered individual). The administrator sets the predetermined threshold such that the false acceptance rate and the false rejection rate are both acceptable to the users of the system  100 . 
     In addition, with the sufficient match, the authentication module  128  transmits the candidate set of the biometric data to the augmentor module  132 , which in turn modifies the current reference set of biometric data (e.g., template  136 ) using the candidate set of biometric data. The modification can include several different aspects of the reference biometric data. For example, one aspect is the spatial aspect (e.g., the associated data representing geometric features) of the reference set of the biometric data. Another aspect is the statistical aspect (e.g., the weighting and/or confidence level of features) of the reference set of the biometric data. 
       FIG. 2  illustrates an exemplary embodiment of a supertemplate  200  used to authenticate a user in accordance with the invention. The supertemplate  200  represents a set of biometric data corresponding to a complete set of biometric data. For example, in a fingerprint system, the supertemplate  200  represents the complete set of biometric data for one digit. Superimposed on the supertemplate  200  are a first set of biometric data  205 , a second set of biometric data  210 , a third set biometric data  215  and a fourth set of biometric data  220 . As illustrated, the sets of biometric data  205 ,  210 ,  215  and  220  are smaller in size than the supertemplate  200 . In one embodiment, the sets of biometric data  205 ,  210 ,  215  and  220  represent templates  136 . The supertemplate  200  can comprise one or more templates  136 . The size of the sets of biometric data  205 ,  210 ,  215  and  220  are based on the biometric input device  160 . For example, the size of the scanner, the size of the local memory and the like. It is noteworthy that even if the scanner is large enough to cover the entire finger the supertemplate  200  can accumulate additional information from multiple templates  205 ,  210 ,  215  and  220  to generate more accurate statistics for the features. 
     For an illustrative example of the modifying process, a reference set of biometric data is the supertemplate  200  and a candidate set of biometric data for a first authentication request is equivalent to the biometric data represented in the first set of biometric data  205 . As described above, upon a sufficient match, the augmentor module  132  modifies the supertemplate  200  using the candidate set of biometric data. For the sufficient match, the authentication module  128  matches features of the candidate set of biometric data with features of the reference data included in the supertemplate  200 . The augmentor module  132  aligns those matched features to determine how the candidate set of biometric data fits into the supertemplate  200 . 
     When the augmentor module  132  determines the alignment, the augmentor  132  modifies the template  200  using the candidate set of biometric data. The results are that in this illustrative example, the area indicated as the first set of biometric data  205  is modified with the candidate set of biometric data. In one embodiment, the augmentor module  132  modifies by replacing the features in the existing reference biometric data in the area indicated as the first set of biometric data  205  with the candidate set of biometric data received by the client  112 . In this way, the system accommodates feature changes that occur over time (e.g., due to aging of the user). In another embodiment, the augmentor module  132  augments the existing reference biometric data by adding in those features of the candidate set of biometric data that are not matched and/or not presently included in the reference set of biometric data. This allows the system  100  to build a filler biometric representation than would be possible with, for example, a single scan by the biometric input device  160 . Augmented in this fashion, the supertemplate  200  can evaluate scans covering different portions of, for example, the user&#39;s fingerprint, increasing the system&#39;s tolerance for variation without sacrificing accuracy (i.e., the number of feature points matched). 
     Continuing with the illustrative example of the modifying process, a candidate set of biometric data for a second authentication request is equivalent to the biometric data represented in the second set of biometric data  210 . As described above, upon a sufficient match, the augmentor module  132  aligns those matched features to determine how the candidate set of biometric data fits into the supertemplate  200 . Once the augmentor module  132  determines the alignment, the augmentor  132  modifies the template  200  using the candidate set of biometric data. The results are that in this illustrative example, the area indicated as the second set of biometric data  210  is modified with the candidate set of biometric data. Similarly in subsequent authentication requests, the augmentor module  132  modifies the areas indicated as the third and fourth sets of biometric data,  220 ,  225  respectively. 
     As described above, in addition to the modification of features, the augmentor module  132  also modifies the statistical parameters of the reference set of biometric data. Each time features in a candidate set of biometric data match the features of the reference set biometric data, the augmentor module  132  increases the weighting and/or confidence level of those matched features. The area  230 , indicated by shading, represents the overlap of all of the sets of biometric data ( 205 ,  210 ,  215  and  225 ). The weighting and/or confidence level of the matched features in this area  230  is the highest, as it has been reinforced by the redundant presence of the matched features in each of the four received candidate sets of biometric data. The closeness of the match can also affect the value of the weighting and/or confidence level. For example those features that directly overlap with two candidate sets of biometric data have a higher weighting and/or confidence level than those features that are close, but have some small distance between them. 
     In broad overview,  FIG. 3  illustrates another embodiment of system  100 ′ to authenticate a user using augmented biometric data in accordance with the invention. The server node  108 ′ of the system  100 ′ includes a network interface module  124 ′, an authentication module  128 ′, a storage module  135 , having a template  136 ′ stored therein, and a modification module  320 . The client node  112 ′ of the system includes a client comparator module  330 . 
     To use the system  100 ′, the user  170  registers that user&#39;s  170  biometric data with the system  100 ′, as described above. For authenticating, the server  108 ′ and client  112 ′ use a challenge-response protocol that does not transmit a full set of biometric data across the network  116 . This challenge-response protocol modifies a portion of the set of biometric data sent across the network  116  so that if intercepted by someone, it is not usable in its modified state.  FIG. 4  depicts a supertemplate  200 ′ that the system  100 ′ employs to implement the challenge-response protocol. The supertemplate  200 ′ includes a challenge template  405  that represents a set of biometric data. In one embodiment, the challenge template  405  is equivalent in area to the template  136 ′. As described above, the template  136 ′ varies in size and is at least a portion of the supertemplate  200 ′. The challenge template  405  includes a first portion  410  and a second portion  420 . As illustrated, the first portion  410  and the second portion  420  include random feature data, as described in more detail below. 
       FIG. 5  illustrates an embodiment of a process  500  to authenticate a user  170  using the challenge-response protocol, a system  100 ′ as depicted, for example, in  FIG. 3  and a challenge template  405  as depicted, for example, in  FIG. 4 . In operation, the client  112 ′, in response to a user  170  action, generates (step  505 ) a request. The request can be an authentication request directly from the client  112  to authenticate the user  170 . The request can also be a service request for a certain service (e.g., execution of an application program, access to a financial or medical database, access to an electronic vault with which the user  170  is associated, download of data and/or an application program, and the like) provided by a server on the network (e.g.,  108 ′ or a different application server). In that case, the server providing the requested service transmits a request for authentication to the authentication module  128 ′. 
     In response to the authentication request, the modification module  320  copies (step  510 ) the template  136 ′ of the reference biometric data associated with the user  170 . The modification module  320  generates (step  515 ) modification data and uses this modification data to modify (step  520 ) the copy of the template to generate a challenge template  405 . For an illustrative example, the modification module  320  copies (step  510 ) at least a portion of the geometric data contained within the reference template  136 ′, for example, the x, y coordinates of the features. To generate the modification data, the modification module  320  generates random x, y locations and thereby generates random modified features at these locations in the challenge template  405 . In another embodiment, the modification data is not random but generated by an algorithm that is dependent on the biometric data, thus creating different modification data for different users. The modification module  320  modifies (step  520 ) the copy of the reference template (i.e., challenge template  405 ) by inserting the modification data into the challenge template  405 , for example at the random x, y locations. The modification module  320  can also create the modification data used for the challenge template  405  by combining features from other users or other fingers to create a composite that is similar to real data because the modification data is based on real biometric data. For example, the modification module  320  can create the composite modification data from other users and then align the end points when inserting this composite modification data in portion  410 , so it looks like real data, but would not be matchable without knowing which areas were false. 
     For clarity and illustration only, the modification data for this particular request of the illustrated process  500  fall within the first and second portions,  410  and  420  respectively, of the challenge template  405 . Of course, if the modification data were random, then the modified x, y coordinates would be distributed randomly throughout the challenge template area. In another embodiment, the modification module  320  can insert random noise in portions of the challenge template  405 , for example, in the first and second portions,  410  and  420  respectively. Once the modification module  320  generates the challenge template  405 , the server  108 ′ transmits the challenge template  405  to the client  112 . As stated above, with random data inserted in the first location  410  and the second location  420 , even if the challenge template  405  is copied by an eavesdropper, the challenge template  405  is not usable because the biometric data in those locations will not match reference biometric data (e.g., reference template  136 ′) stored for that user in biometric authentication systems. 
     The comparator module  330  of the client  112 ′ receives (step  530 ) a set of candidate biometric data from the biometric input device  160 . The comparator module  330  compares (step  535 ) the candidate set of biometric data with the received challenge template  405 . The comparator module  330 , for example, can spatially align the candidate set of biometric data with the challenge template  405 , maximizing the number of matching features, and then calculate a degree of overlapping (i.e., matching) of the features at various x, y coordinates. The comparator module  330  generates (step  540 ) a response vector, for example, listing the x, y coordinates and the degree of matching. Another format can include the actual candidate features found in all matching areas. Other formats for the response vector include listing the x, y coordinates that are above (or below) a certain threshold, listing the x, y coordinates with no matching features, generating a hash code using the challenge template  405  and the candidate set of biometric data, and the like. The client  112 ′ transmits the response vector back to the server  108 ′. The transmitted response vector does not include a full set of biometric data, so it is not usable if someone intercepts it. 
     The authentication module  128 ′ receives the response vector and compares (step  550 ) the response vector with the modification data. The authentication module  128 ′ determines (step  555 ) if the comparison between the response vector and the modification data indicates that there is a sufficient match, or in other words, that the user  170  is, to a statistical degree of certainty, the registered individual. If the authentication module  128 ′ determines that the comparison indicates there is not a sufficient match, the authentication module  128 ′ denies (step  560 ) the user  1780  as the registered individual. If the authentication module  128 ′ determines that the comparison indicates there is a sufficient match, the authentication module  128 ′ authenticates (step  565 ) the user  1780  as the registered individual. 
     Ideally, when the user  170  is the registered individual, the mismatches identified in the response vector coordinate with the modification data in the first location  410  and the second location  420 , while the features outside of these locations match to a high degree of probability. Deviations from this ideal can be caused by noise introduced by the biometric input device  160 , different sizes of the candidate set of biometric data and the challenge template  405 , rotation and/or motion of the user&#39;s finger while scanning, and the like. As described above, the authentication module  128 ′ statistically analyzes the mismatches, accounting for those due to the modification data, and determines to a statistical certainty whether the matches indicate that the user  170  is the registered individual. 
     EQUIVALENTS 
     The invention can be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting on the invention described herein. Scope of the invention is thus indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.