Patent Publication Number: US-2022239644-A1

Title: Systems and methods for authenticating a user based on a biometric model associated with the user

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is a continuation of U.S. application Ser. No. 16/883,636, filed May 26, 2020, which is a continuation of U.S. application Ser. No. 16/196,884, filed Nov. 20, 2018, issued as U.S. Pat. No. 10,666,648 on May 26, 2020, which is a continuation of U.S. application Ser. No. 15/823,506, filed Nov. 27, 2017, and issued as U.S. Pat. No. 10,135,821 on Nov. 20, 2018, which is a continuation of U.S. application Ser. No. 14/929,647, filed Nov. 2, 2015, and issued as U.S. Pat. No. 9,832,191 on Nov. 28, 2017, which is a continuation of U.S. application Ser. No. 14/033,851, filed Sep. 23, 2013, and issued as U.S. Pat. No. 9,203,835 on Dec. 1, 2015, and which claims priority to U.S. Provisional Application Serial No. 61/771,785, filed Mar. 1, 2013, all of which applications are incorporated in their entirety herein by reference. 
    
    
     BACKGROUND 
     Technical Field 
     Embodiments disclosed herein are related to systems and methods for authenticating a user based on a biometric model associated with the user. In particular, systems and methods disclosed herein may create a biometric model associated with a user, and use the created model to authenticate a user. 
     Related Art 
     Known biometric authentication techniques include fingerprint sensors and hand signature sensors. Fingerprint sensors are included with some personal computing devices and require a user to place or swipe a finger on the sensor. The data obtained by the sensor is compared to a stored or external template or model to determine user identity based on a probability that certain indicia within the template are met. Hand signature authentication techniques require a user to sign on an electronic pad. Indicia about the received signature, including a speed, pressure, and the actual pattern represented by the signature are analyzed to determine a probability of the indicia matching. Iris and other optical authentication is similar, where an optical scan is taken, and certain indicia are matched to a template. These techniques are all risk-based authentication techniques where a determination is made as to whether certain features are met based on a predetermined threshold. 
     Biometric authentication methods based on indicia of a user&#39;s hand have been recently discloses. The disclosed methods require the user to perform an action with the hand many times that is recorded by a biometric sensor. Then, to authenticate, the user is again asked to perform the action. The performed action is compared to the recorded actions to determine how closely the performed action matches the recorded actions, wherein enough similarity results in a successful authentication. The idea is that due to the differences between individual user&#39;s hands, there is enough entropy that even if an attacker sees a user performing the action, it is unlikely that the attacker has the same hand and, thus, the action will be performed differently. However, this authentication method does not understand what a hand looks like, but only asks that the user memorize the action so that they can perform it when asked. 
    
    
     
       BRIEF DESCRIPTION OF THE FIGURES 
         FIG. 1  is a block diagram of a networked system, consistent with some embodiments. 
         FIG. 2  is a diagram illustrating computing system, consistent with some embodiments. 
         FIG. 3  is a diagram illustrating a biometric model of a user&#39;s hand, consistent with some embodiments. 
         FIG. 4  is a diagram illustrating an example of a challenge that may be used to create a biometric model, consistent with some embodiments. 
         FIG. 5  is a diagram illustrating user performing the challenge shown in  FIG. 4 , consistent with some embodiments. 
         FIG. 6  is a diagram illustrating another challenge that may be used to create a biometric model, consistent with some embodiments. 
         FIG. 7  is a diagram illustrating a user performing a challenge with optical biometric sensors. 
         FIG. 8  is a flowchart illustrating a process for authenticating a user using a biometric model, consistent with some embodiments. 
         FIG. 9  is a flowchart illustrating a process for creating a biometric model, consistent with some embodiments. 
     
    
    
     In the drawings, elements having the same designation have the same or similar functions. 
     DETAILED DESCRIPTION 
     In the following description specific details are set forth describing certain embodiments. It will be apparent, however, to one skilled in the art that the disclosed embodiments may be practiced without some or all of these specific details. The specific embodiments presented are meant to be illustrative, but not limiting. One skilled in the art may realize other material that, although not specifically described herein, is within the scope and spirit of this disclosure. 
     There is a need for a more secure biometric authentication system that constructs a biometric model associated with the user that is more unique to the user than current templates or models. 
     Consistent with some embodiments, there is provided a system for authenticating a user. The system includes one or more processors configured to determine a biometric model of the user, generate at least one challenge, determine an expected response based on the determined biometric model and the generated challenge, and determine if a received response matches the expected response within a predetermined degree of accuracy. The system also includes a biometric sensor configured to receive the response and a memory storing the determined biometric model. The system further includes a network interface component coupled to a network, the network interface component configured to transmit a successful authentication when the received response matches the expected response within a predetermined degree of accuracy. 
     Consistent with some embodiments, there is further provided a method for authenticating a user. The method includes steps of determining a biometric model of the user, storing the determined biometric model, generating at least one challenge in response to a request for authentication, determining an expected response based on the stored model and the generated at least one challenge, and transmitting a successful authentication when a received response matches the expected response within a predetermined degree of accuracy. The method may be embodied in non-transient computer-readable media. 
     These and other embodiments will be described in further detail below with respect to the following figures. 
       FIG. 1  is a block diagram of a networked system  100 , consistent with some embodiments. System  100  includes a client computing device  102  and a remote server  106  in communication over a network  108 . Remote server  106  may be a payment service provider server that may be maintained by a payment provider, such as PayPal, Inc. of San Jose, Calif. Server  106  may be maintained by other service providers in different embodiments. Remote server  106  may also be maintained by an entity with which sensitive credentials and information may be exchanged with client computing device  102 . Remote server  106  may be more generally a web site, an online content manager, a service provider, such as a bank, or other entity who provides content to a user requiring user authentication or login. 
     Network  108 , in one embodiment, may be implemented as a single network or a combination of multiple networks. For example, in various embodiments, network  108  may include the Internet and/or one or more intranets, landline networks, wireless networks, and/or other appropriate types of communication networks. In another example, the network may comprise a wireless telecommunications network (e.g., cellular phone network) adapted to communicate with other communication networks, such as the Internet. 
     Client computing device  102 , in one embodiment, may be implemented using any appropriate combination of hardware and/or software configured for wired and/or wireless communication over network  108 . For example, client computing device  102  may be implemented as a wireless telephone (e.g., smart phone), tablet, personal digital assistant (PDA), notebook computer, personal computer, a connected set-top box (STB) such as provided by cable or satellite content providers, or a video game system console, a head-mounted display (HMD) or other wearable computing device, and/or various other generally known types of computing devices. Consistent with some embodiments, client computing device  102  may include any appropriate combination of hardware and/or software having one or more processors and capable of reading instructions stored on a tangible non-transitory machine-readable medium for execution by the one or more processors. Consistent with some embodiments, client computing device  102  includes a machine-readable medium, such as a memory (not shown) that includes instructions for execution by one or more processors (not shown) for causing client computing device  102  to perform specific tasks. For example, such instructions may include authentication app  112  for authenticating client computing device  102  to remote server  106 . Consistent with some embodiments, authentication app  112  may be a mobile authentication app, which may be used to authenticate user  120  to remote server  106  over network  108 . Authentication app  112  may include a software program, such as a graphical user interface (GUI), executable by one or more processors that is configured to interface and communicate with the remote server  106  or other servers managed by content providers or merchants via network  108 . 
     Client computing device  102  may also include biometric model application  114  for creating a biometric model and issuing biometric challenges based on the created model for authenticating a user of computing device  102  to remote server  106 . Client computing device  102  may also include sensor applications  116 . Consistent with some embodiments, sensor applications  116  include applications which utilize sensor capabilities within client computing device  102  to monitor characteristics of device  102 , user  120 , and/or the environment surrounding client computing device  102 . Such characteristics include obtaining images (video or still) of user  120  using camera functionalities of client computing device  102 , obtaining accelerometer readings using an accelerometer in client computing device  102 , using a geographical location of user  120  and/or client mobile device using global positioning system (GPS) functionality of client computing device  102  and/or obtaining a relative location using an internet protocol (IP) address of client computing device  102 . Consistent with some embodiments, characteristics of client computing device  102 , user  120 , and/or the environment around client computing device  102  may be captured using sensor applications  116  and used by authentication app  112  for authentication purposes. Further, sensor applications  116  may work with biometric model application  114  and authentication application  112  for capturing and creating a biometric model of user  120  for use in authentication with remote server  106 . 
     Client computing device  102  may also include other applications  118  as may be desired in one or more embodiments to provide additional features available to user  120 , including accessing a user account with remote server  106 . For example, applications  118  may include interfaces and communication protocols that allow the user to receive and transmit information through network  108  and to remote server  106  and other online sites. Applications  118  may also include security applications for implementing client-side security features, programmatic client applications for interfacing with appropriate application programming interfaces (APIs) over network  108  or various other types of generally known programs and/or applications. Applications  118  may include mobile apps downloaded and resident on client computing device  102  that enables user  120  to access content through the apps. 
     Remote server  106  according to some embodiments, may be maintained by an online payment provider, which may provide processing for online financial and information transactions on behalf of user  120 . Remote server  106  may include at least authentication application  122 , which may be adapted to interact with authentication app  112  of client computing device  102  over network  108  to authenticate client computing device  102  to remote server  106 . Remote server  106  may also include an application database  124  for storing various applications for interacting with client computing device  102  over network  108  for purposes other than authentication. Such applications may include applications for authentication, conducting financial transactions and shopping and purchasing items. 
       FIG. 2  is a diagram illustrating computing system  200 , which may correspond to any of client computing device  102  or remote server  106 , consistent with some embodiments. Computing system  200  may be a mobile device such as a smartphone, a tablet computer, a personal computer, laptop computer, netbook, or tablet computer, set-top box, video game console, head-mounted display (HMD) or other wearable computing device as would be consistent with client computing device  102 . Further, computing system  200  may also be a server or one server amongst a plurality of servers, as would be consistent with remote server  106 . As shown in  FIG. 2 , computing system  200  includes a network interface component (NIC)  202  configured for communication with a network such as network  108  shown in  FIG. 1 . Consistent with some embodiments, NIC  202  includes a wireless communication component, such as a wireless broadband component, a wireless satellite component, or various other types of wireless communication components including radio frequency (RF), microwave frequency (MWF), and/or infrared (IR) components configured for communication with network  108 . Consistent with other embodiments, NIC  202  may be configured to interface with a coaxial cable, a fiber optic cable, a digital subscriber line (DSL) modem, a public switched telephone network (PSTN) modem, an Ethernet device, and/or various other types of wired and/or wireless network communication devices adapted for communication with network  108 . 
     Consistent with some embodiments, computing system  200  includes a system bus  204  for interconnecting various components within computing system  200  and communication information between the various components. Such components include a processing component  206 , which may be one or more processors, micro-controllers, or digital signal processors (DSP), or graphics processing units (GPUs), a system memory component  208 , which may correspond to random access memory (RAM), an internal memory component  210 , which may correspond to read-only memory (ROM), and an external or static memory  212 , which may correspond to optical, magnetic, or solid-state memories. Consistent with some embodiments, computing system  200  further includes a display component  214  for displaying information to a user  120  of computing system  200 . Display component  214  may be a liquid crystal display (LCD) screen, an organic light emitting diode (OLED) screen (including active matrix AMOLED screens), an LED screen, a plasma display, or a cathode ray tube (CRT) display. Computing system  200  may also include an input component  216 , allowing for a user  120  of computing system  200  to input information to computing system  200 . Such information could include payment information such as an amount required to complete a transaction, account information, authentication information, or identification information. An input component  216  may include, for example, a keyboard or key pad, whether physical or virtual. Computing system  200  may further include a navigation control component  218 , configured to allow a user to navigate along display component  214 . Consistent with some embodiments, navigation control component  218  may be a mouse, a trackball, or other such device. Moreover, if device  200  includes a touch screen, display component  214 , input component  216 , and navigation control  218  may be a single integrated component, such as a capacitive sensor-based touch screen. 
     Computing system  200  may perform specific operations by processing component  206  executing one or more sequences of instructions contained in system memory component  208 , internal memory component  210 , and/or external or static memory  212 . In other embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the present disclosure. Logic may be encoded in a computer readable medium, which may refer to any medium that participates in providing instructions to processing component  206  for execution. Such a medium may take many forms, including but not limited to, non-volatile media or volatile media. The medium may correspond to any of system memory  208 , internal memory  210  and/or external or static memory  212 . Consistent with some embodiments, the computer readable medium is tangible and non-transitory. In various implementations, non-volatile media include optical or magnetic disks, and volatile media includes dynamic memory. Some common forms of computer readable media include, for example, floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic medium, CD-ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, RAM, PROM, EPROM, FLASH-EPROM, any other memory chip or cartridge, or any other medium from which a computer is adapted to read. 
     In various embodiments of the present disclosure, execution of instruction sequences to practice the present disclosure may be performed by computing system  200 . In various other embodiments of the present disclosure, a plurality of computing systems  200  coupled by a communication link  220  to network  108  (e.g., such as a LAN, WLAN, PTSN, and/or various other wired or wireless networks, including telecommunications, mobile, and cellular phone networks) may perform instruction sequences to practice the present disclosure in coordination with one another. Computing system  200  may transmit and receive messages, data and one or more data packets, information and instructions, including one or more programs (i.e., application code) through communication link  220  and network interface component  202 . Communication link  220  may be wireless through a wireless data protocol such as Wi-Fi™, 3G, 4G, HSDPA, LTE, RF, NFC, or through a wired connection. Network interface component  202  may include an antenna, either separate or integrated, to enable transmission and reception via communication link  220 . Received program code may be executed by processing component  206  as received and/or stored in memory  208 ,  210 , or  212 . 
     Computing system  200  may also include sensor components  222  and biometric sensors  224 . Sensor components  222  and biometric sensors  224  may provide sensor functionality for sensor apps  116  and biometric model app  114 , and may correspond to sensors built into client computing device  102  or sensor peripherals coupled to client computing device  102 . Sensor components  222  may include any sensory device that captures information related to the surroundings of client computing device  102 . Sensor components  222  may include camera and imaging components, accelerometers, GPS devices, motion capture devices, and other devices that are capable of providing information about client computing device  102 , user  120 , or their surroundings. Biometric sensors  224  may include biometric readers, optical sensors such as camera devices, capacitive sensors such as may be found in a capacitive touch screen, pressure sensors, fingerprint readers, hand scanners, iris scanners, electromagnetic sensors that detect the EM field produced by a hand, and the like. Consistent with some embodiments, sensor components  222  and biometric sensors  224  may be configured to work with sensor applications  116  and biometric model application  114  to collect biometric information from user  120  that may be used to create a biometric model associated with user  120  that may be used for authenticating to remote server  106 . 
       FIG. 3  is a diagram illustrating a biometric model of a user&#39;s hand, consistent with some embodiments. As shown in  FIG. 3 , model  300  includes unknowns that may be determined by requiring user  120  to perform challenges that are designed to determine the value of the unknowns and complete the model. Some of the values may be related by equations and, thus, some unknown values may be determined through the determination of other values. Model  300  may include such values as a distance from the finger joints on the hand to the finger tips, angles of the fingers on the hand, the maximum lengths of the fingers, the length of the thumb, and a basis line from which a thumb can move, some of which are shown in  FIG. 3 . For example, the little finger has a length of d 1  and can move radially over an angle γ. The ring finger has a length of d 2 , and can move radially over an angle δ. The middle finger has a length of d 3  and can move radially over an angle ε. The index finger has a length of d 4  and can move radially over an angle ζ. The thumb has a length of d 5  and can move radially over an angle η. Moreover, there is an angle β between one side of the palm and the other side. Further, the thumb can move radially outward from the palm to create an angle α 0  with a centerline C of the palm. These distances and angles, once determined, may be used to define a model of the hand that may uniquely identify user  120 . Consequently, once the model has sufficient data to ensure a reasonably probability that user  120  is who they are claiming to be, authentication app  112  may use the biometric model for authentication to remote server  106  based on the probability. The model shown in  FIG. 3  is just one biometric model that may be used for authentication purposes. Different biometric models with different unknowns and degrees of security may be constructed and used for authentication, and such models are within the spirit and scope of this disclosure. 
       FIG. 4  is a diagram illustrating an example of a challenge that may be used to create a biometric model, consistent with some embodiments. As shown in  FIG. 4 , a challenge may be displayed by a display component  214  of client computing device  102  that asks user  120  to align a biometric identifier with respect to biometric sensors  224 . As particularly shown in  FIG. 4 , the challenge requires user  120  to align their fingertips with the indicated areas displayed on display component  214  by placing their fingers on the indicated areas of display component  214 . The challenge may include labeled circles indicating which finger to place in which circle, as shown in  FIG. 4 . The challenge may also include displayed instructions, such as shown in  FIG. 4 . According to some embodiments, other instructions may be displayed in order to provide user  120  with enough information to perform the displayed challenge. Information concerning the challenge may also be displayed to user. This information may include an indication when biometric sensor  224  has detected that user  120  has aligned a biometric identifier as required by the challenge. This information may also be provided by a displayed alert or an audible alert providing information to user  120 , or a displayed timer informing user  120  of how much time user has to complete the alignment. The displayed challenge may be generated by processing component  206  executing instructions associated with biometric model application  114 . The generated challenge may be adaptive to attempt to obtain information that is not known about user  120 . In particular, the generated challenge may be a challenge designed to reduce an error with which biometric application  114  is able to identify user  120  within a predetermined probability. The challenge may be part of a training session designed to capture sufficient information to create a model, such as model  300 , or as part of a game that is designed to capture the same information. Consistent with some embodiments, the challenges are constrained challenges, meaning that there is a predetermined constraints associated with a response. Moreover, the challenges may be designed to model a three dimensional object, such as a hand, in two dimensions. 
       FIG. 5  is a diagram illustrating user performing the challenge shown in  FIG. 4 , consistent with some embodiments. As shown in  FIG. 5 , user  120  has placed their fingertips in the areas indicated by the challenge shown in  FIG. 4 . Consistent with some embodiments, display component  214  is a touch screen device having sensors for detecting a touch of user and, thus, biometric sensors  224  and/or other sensors  222  are integrated into display component  214  and may be part of or a particular function associated with display component  214  and may be configured to detect a touch of user&#39;s  120  hand. Based on the detected locations, some of the unknowns shown in  FIG. 3  may be determined to create the biometric model of user&#39;s  120  hand. Performing the challenge, such as shown in  FIG. 5 , may produce a response that has some truth and error associated therewith. Processing component  206  may use normal statistical methods for fitting the response to the model such as model  300  to provide a model that has a maximum probability of corresponding to user  120 , with a minimum associated error. 
       FIG. 6  is a diagram illustrating another challenge that may be used to create a biometric model, consistent with some embodiments. As shown in  FIG. 6 , the challenge requests that user  120  place their fingertips in similar locations to those shown in  FIG. 4 . The challenge also requests that user  120  move radially towards their palm. This movement may be detected by biometric sensors  224  and/or other sensors  222  and used to determine additional information about model  300  such as the unknowns of α and η. Consistent with some embodiments, display component  214  is a touch screen device having sensors for detecting a touch of user and, thus, biometric sensors  224  and/or other sensors  222  are integrated into display component  214  and may be part of or a particular function associated with display component  214 . 
     The generated challenge may also be used to authenticate user  120 . Once computing device  102  has sufficient information to reasonably identify user  120  beyond a predetermined probability, computing device  102  may generate challenges that fit the created model and should be uniquely identified with user  120 . Consequently, performing the challenge shown in  FIG. 6  may authenticate user  120  to remote server  106 . 
       FIG. 7  is a diagram illustrating a user performing a challenge with optical biometric sensors. As described previously biometric sensors  224  and other sensors  222  may include optical sensors such as a camera that may be configured for sensing a position and depth of field of user  120  for creating a two-dimensional model  300  from a three-dimensional object. The camera may be coupled to a set-top box, a personal or laptop computer, or a wearable computing device having a head-mounted display such as an eyeglass projection sensor. As shown in  FIG. 7 , computing device  102  may generate a similar challenge to the challenge that was generated on the touch screen device shown in  FIGS. 4-6 , but adapted for an optical or other biometric sensor shown in  FIG. 7 . In particular display component  214  may display the generated challenge along with instructions that allow biometric sensors  224  in combination with other sensors  222  to obtain sufficient information about user  120  to create a biometric model, such as model  300 , and then authenticate user using the created model. The instructions may include text, a displayed motion demonstrating the challenge, or a voice command, providing instructions for aligning a biometric identifier to complete the challenge, and may also include an indication when the alignment is complete and a timer indicating how long user  120  has left to complete the challenge. 
       FIG. 8  is a flowchart illustrating a process  800  for authenticating a user using a biometric model, consistent with some embodiments. For the purpose of illustration,  FIG. 8  will be described with reference to any of  FIGS. 1-7 . Process  800  shown in  FIG. 8  may be embodied in computer-readable instructions for execution by one or more processors in processing component  206  such that the steps of process  800  may be performed by client computing device  102 . As shown in  FIG. 8 , process  800  begins by determining a biometric model ( 802 ). Consistent with some embodiments, determining a biometric model may include issuing at least one challenge and receiving responses to the challenges. The at least one issued challenge may be part of a training set up that may be performed during an initial set up of client computing device  102 . The responses may be issued in order to learn unknown values of the model. For example, to determine model  300 , challenges may be issued in order to learn distances d 1 -d 5  and radial values α, β, δ, γ, ε, ζand η, which may be an angle or a radial distance. Moreover, the issued challenges may be adaptive such that a successive challenge attempts to determine values that a previous response did not provide. Further, the challenges may determine which values have the greatest source of error and issue challenges in order to attempt to reduce this error. A method for determining a biometric model is described in additional detail below with reference to  FIG. 9 . 
     After a model has been determined, the model is stored ( 802 ). Consistent with some embodiments, the model may be stored locally in client computing device  102  in any of memories  208 ,  210 , and  212 . In some embodiments, the model may be stored on remote server  106 , which may a service provider server or an identity clearinghouse server. While the model may be stored, it may be modified over time to change and adapt with user  120  as they change or age by issuing additional challenges to retrain and re-determine the model. Computing device  102  may then request authentication to remote server  106  ( 806 ). According to some embodiments, the request for authentication is issued to remote server in response to user  120  attempting to access features provided by remote server  106  using computing device  102 . In response to the request for authentication, authentication app  122  on remote server may send a message that triggers authentication app  112  of computing device to generate at least one challenge ( 808 ). According to some embodiments, the at least one challenge may be generated based on the stored biometric model. 
     After the challenge is generated, processing component  206  of computing device may calculate an expected response based on the stored biometric model and the generated challenge ( 810 ). Computing device  102  may then receive a response to the generated challenge ( 812 ). Consistent with some embodiments, the response may be received by biometric sensors  224  or other sensors  222 , or a combination thereof. Further, the response may be received by display component  214  which may be a touch screen device that incorporates biometric sensors  224 . The response may also be received by biometric sensors  224  that are separate from but coupled to computing device  102 . Processing component may then determine if the received response fit the calculated expected response within a predetermined degree of accuracy ( 814 ). According to some embodiments, if the response does not fit the expected response within a predetermined degree of accuracy, computing device  102  may generate a subsequent challenge based on the stored model ( 808 ). However, if the response fits the expected response within a predetermined degree of accuracy, computing device  102  may transmit an indication to remote server  106  that the authentication was successful ( 816 ). According to some embodiments, the transmitted indication may be in the form of a token, certificate, or other secure identifier of a successful authentication. 
     According to some embodiments, the predetermined degree of accuracy achieved by the response may determine the level of access user  120  has to services offered by remote server  106 . For example, a very high degree of accuracy achieved (i.e., very small error), indicates a very high probability that user  120  is who they are claiming to be, and more services, or a higher tier of services, may be provided to user  120 . Similarly, a low, but passing, degree of accuracy indicating higher error, may indicate a good probability that user  120  is who they are claiming to be and may allow a lower tier of services. A geographical location of user  120  may also be captured and used to determine a user&#39;s  120  access to services based on a known or past location of user  120 . Tiered authentication is further described in U.S. application Ser. No. 13/605,886, filed on Sep. 6, 2012, the entire contents of which are hereby incorporated by reference in their entirety. 
       FIG. 9  is a flowchart illustrating a process  900  for creating a biometric model, consistent with some embodiments. For the purpose of illustration,  FIG. 9  will be described with reference to any of  FIGS. 1-7 . Process  900  shown in  FIG. 9  may be embodied in computer-readable instructions for execution by one or more processors in processing component  206  such that the steps of process  900  may be performed by client computing device  102 . As shown in  FIG. 9 , process  900  begins by generating a challenge ( 902 ). According to some embodiments, the challenge may be generated by biometric model application  114 , and may be a challenge designed to obtain a model that is tailored to computing device  102  and user  120 . For example, if computing device  102  has biometric sensors  224  that are limited in size, the biometric model that may be created may be a more limited model or uses different measurements to obtain the model and, as a result, issues appropriate challenges. Moreover, if computing device  102  uses optical sensors for obtaining biometric readings, such as shown in  FIG. 7 , the generated challenge may be tailored for optical 2D modeling of a 3D object such as a hand of user  120 . 
     Computing device  102  may then receive a response to the generated challenge ( 904 ). Consistent with some embodiments, the response may be received by biometric sensors  224  or other sensors  222 , or a combination thereof. Further, the response may be received by display component  214  which may be a touch screen device that incorporates biometric sensors  224 . The response may also be received by biometric sensors  224  that are separate from but coupled to computing device  102 . Processing component  206  may fit the received response to the model ( 906 ). Processing component  206  may then determine if the model is sufficiently accurate ( 908 ), wherein sufficiently accurate refers to having a model that can identify user within a predetermined degree of accuracy. If the model is sufficiently accurate such that user  120  can be identified using the model with a predetermined degree of accuracy, the model will be stored ( 910 ). Consistent with some embodiments, the model may be stored locally in client computing device  102  in any of memories  208 ,  210 , and  212 . In some embodiments, the model may be stored on remote server  106 , which may a service provider server or an identity clearinghouse server. While the model may be stored, it may be modified over time to change and adapt with user  120  as they change or age by issuing additional challenges to retrain and re-determine the model. 
     If the model is not sufficiently accurate, processing component  206  may determine a point of substantial error in the model ( 912 ). Consistent with some embodiments, a point of substantial error may be a missing distance or angle, or other value in which insufficient information has been obtained. That is, a point of substantial error may be a missing value or value having insufficient information such that a user  120  cannot be identified using a model within a predetermined degree of accuracy. Once a point of substantial error has been determined, a challenge that attempts to address the determined substantial point of error is generated ( 914 ). Consequently, biometric model application  114  executed by one or more processors of processing component  206  may repeat steps  904 ,  906 ,  908 ,  912 , and  914  until the model is determined to be sufficiently accurate, and then stored. That is, the generated challenges may be adaptive so that they attempt to address substantial points of error in the model to improve the model until it is sufficient so that user  120  can be identified using the model within a predetermined degree of accuracy. 
     Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more machine-readable mediums, including non-transitory machine-readable medium. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein. 
     Consequently, embodiments as described herein may provide systems and methods for authentication based on a biometric model of a user. In particular, embodiments as described herein determine a biometric model of a user and then generate challenges based on the biometric model. The response to the challenges may be fit to the model, an error associated with the fitted response may be determined, and a user may be authenticated if the error is within a predetermined range. Moreover, the user may be granted tiered authentication based on the error, wherein lower error provides greater privileges, and higher error provides lesser privileges. The examples provided above are exemplary only and are not intended to be limiting. One skilled in the art may readily devise other systems consistent with the disclosed embodiments which are intended to be within the scope of this disclosure. As such, the application is limited only by the following claims.