Patent Abstract:
An articulated and animated toy capable of recognizing human users and interacting therewith includes a computer-based device storing encoded first human fingerprint data, a fingerprint sensor for acquirin data representative of a second human fingerprint, and software for fingerprint verification. The apparatus can further include software for recognizing speech, generating speech and controlling animation of the articulated toy. In addition, the toy is capable of learning and storing information pertaining to each of said human users such as name, age, sex, favorite color, etc., and to interact with each of said human users on an individual basis, providing entertainment tailored specifically to each of said human users.

Full Description:
RELATED APPLICATION INFORMATION 
     This application is a continuation of U.S. patent application Ser. No. 09/588,085 filed Jun. 6, 2000, now U.S. Pat. No. 6,807,291, and claims priority to provisional application 60/137,569 filed Jun. 4, 1999, both of which are herein incorporated by reference. 
    
    
     FIELD OF THE INVENTION 
     The present invention is generally directed to an apparatus and method for integrating a fingerprint sensor and computer-based algorithm with an articulated and animated toy capable of recognizing a human user, and providing entertainment and interaction with said human user in response thereto. In addition, said computer-based toy can learn and store in resident memory, specific information about said human user and further access and recall said information for use in interacting with said human user, such as integrating personal information about said user into a story or game, or controlling access to the Internet after said user is identified. 
     BACKGROUND 
     There are a number of new articulated and animated toys capable of interacting with human users in a way which appears intelligent which are well known in the art and commercially available under such trademarks as Furby® from Tiger Electronics, Ltd., and Barney® from MicroSoft Inc. These toys are capable of understanding speech, speaking in a natural language and demonstrating limited animation such as mouth, eye and ear movements. In addition, prior to the development of these more sophisticated toys, which generally include an embedded microprocessor and computer-based algorithm, other predecessors such as that commonly known under the trademark Teddy Ruxpin™ from YES! Entertainment Corporation, are also capable of exhibiting semi-intelligent behavior through speech and animation. Teddy Ruxpin™, and other toys like it, utilize a tape mechanism to provide the sound and animation control. Without exception, to date, a toy has never been developed which is capable of recognizing the human user who is playing with the toy. The advantage of such capability is immediately obvious as it increases the sophistication and intelligence of a toy to levels heretofore unseen. A toy with the capability of recognizing its human user can learn specific information about said human user and interact individually with a number of said human users by providing tailored entertainment. In addition, toys capable of recognizing an individual human user could control access to the Internet through integrated web browser software and thus provide protection, especially for young children, from inappropriate web site content. 
     There exists many methods for creating the semblance of intelligence in a toy or computer game. Toys with animated moving parts are commonplace and anyone of ordinary skill in the art will be familiar with several methods to fabricate quasi-intelligent articulated toys. Similarly there exists many methods for the biometric identification of humans which includes face recognition, voice recognition, iris scanning, retina imaging as well as fingerprint verification. 
     Iris and retina identification systems are considered “invasive”, expensive and not practical for applications such as integrating with a toy where limited computer memory storage is available and manufacturing costs must be minimized. Voice recognition, which is not to be confused with speech recognition, is somewhat less invasive, however it is cost prohibitive and can require excessive memory storage space for the various voice “templates”. In addition, identification processing delays can be excessive and unacceptable for many applications. 
     Fingerprint verification is a minimally invasive way to identify a human user. A fingerprint verification and identification system can be constructed in such a way that its operation is simple and natural for a human user. With recent advances in the performance of inexpensive single board computers and embedded microprocessors, it has become possible to implement a practical and cost effective fingerprint verification system for use in providing human user recognition for toys or computer games. 
     Although many inventors have offered approaches to verifying human fingerprints for recognizing human users, none have succeeded in producing a system that would be viable for use in an articulated and animated toy or computer game. Part of the reason for this lies in the severe constraints imposed on the sensor apparatus such as size and physical configuration. Another reason is that the complexity of the algorithms and the hardware necessary to implement them makes such a recognition system cost prohibitive for use with a toy. 
     The present invention overcomes these limitations by combining streamlined algorithms with advanced microprocessor architectures. The algorithms of the present invention have been optimized to run quickly on small inexpensive single board computers and embedded microprocessors. 
     SUMMARY 
     It is an object of the present invention to improve the apparatus and method for fingerprint verification of human users for use with articulated and animated toys or computer games. 
     It is another object of the present invention to improve the apparatus and method for creating the semblance of intelligence in an articulated and animated toy or computer game. 
     It is still another object of the present invention to improve the method for providing protection, especially for young children, from inappropriate Internet web site content. 
     Accordingly, one embodiment of the present invention is directed to an apparatus for an articulated and animated toy capable of recognizing human users and interacting therewith which includes a computer-based device having stored thereon encoded first human fingerprint data, a fingerprint sensor for acquiring data representative of a second human fingerprint, and software resident within said computer-based device for fingerprint verification, which includes minutiae analysis, neural networks, or another equivalent algorithm for comparing said first human fingerprint data with said second human fingerprint data and producing an output signal therefrom for use in identifying said human users. The apparatus can further include software for recognizing speech, generating speech and controlling animation of the articulated toy. In addition, said computer-based device is capable of learning and storing information pertaining to each of said human users such as name, age, sex, favorite color, etc., and to interact with each of said human users on an individual basis, providing entertainment tailored specifically to each of said human users. In addition, the apparatus can control access to the Internet via integrated web browser software and thus provide protection, especially for young children, from inappropriate web site content. 
     Other objects and advantages will be readily apparent to those of ordinary skill in the art upon viewing the drawings and reading the detailed description hereafter. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows a block diagram of an aspect of the present invention for integrating a fingerprint sensor with an animated and articulated toy. 
         FIG. 2  shows in functional block diagram a representation of minutiae analysis of the present invention. 
         FIG. 3  shows in functional block diagram a representation of a neural network of the present invention. 
     
    
    
     DETAILED DESCRIPTION 
     Referring to the drawings, an apparatus for an articulated and animated toy capable of recognizing human users  150  and interacting therewith of the present invention is generally referred to by the numeral  100 . Referring now particularly to  FIG. 1 , the apparatus  100  includes a computer  113  having a central processor (CP)  116  such as is well known in the art and commercially available under the trademarks Intel® 486 or Pentium®, conventional non-volatile Random Access Memory (RAM)  114 , conventional Read Only Memory (ROM)  115 , conventional disk storage device  118 , and a sound card  117  such as is commercially available under the trademark SoundBlaster™. Computer  113  can be of a standard PC configuration such as is commercially available under the trademarks Compaq® or Dell®, or can be miniaturized and embedded directly in the toy  127  itself. Computer  113  is further operably associated with interface electronics  119  and fingerprint sensor  120 . The fingerprint sensor  120 , mounted inside the toy  127 , such as a plush teddy bear, doll or sophisticated animated and articulated toy, can be one of many devices well known in the art and available commercially under the trademarks Digital Persona U.areU™, Veridicom OpenTouch™, Thomson FingerChip™, and AuthenTec FingerLoc™. The interface electronics  119  can be one of many off-the-shelf units well known by anyone of ordinary skill in the art and commonly employed in personal computers for the acquisition of digital signals such as a standard RS-232 serial port or Universal Serial Bus (USB). The fingerprint sensor  120  described herein above, can be mounted in the head, belly, back, hand, arm, leg or foot of toy  127 , thus providing a simple means by which a human user  150 , such as a child, can access and operate the toy&#39;s biometric component. 
     The computer  113  further has operably associated therewith fingerprint verification software  140  which compares a first digitized human fingerprint  151 , stored on said disk storage device  118  with a second digitized human fingerprint  152  acquired in real-time from human user  150  and provides a signal indicative of verification or non-verification of human user  150 . The fingerprint verification software  140  can be of one of several algorithms known by anyone who is of ordinary skill in the art such as minutiae analysis  200  or neural network  300  or another equivalent algorithm, the particulars of which are further described hereinafter. 
     A communications cable  121  is likewise associated with the computer  113  and operably connected to interface electronics  119  for providing speech and articulation control signals to interface electronics  119 . If computer  113  is configured as a standard PC, the communications cable  121  will be external, while if computer  113  is embedded directly in the toy  127 , the communications cable  121  will be internal. 
     Interface electronics  119  is operably connected to the toy&#39;s  127  internal control circuits  128 . The control circuit  128  is of a standard type such as is well known to anyone of ordinary skill in the art and employed in several of the toys described in detail herein above, and controls the basic functions of the toy&#39;s  127  articulation, including the animation thereof. Control circuit  128  is operably connected to a battery  129  and electronic servo motors  130 . Servo motors  130  are flexibly coupled to mechanical articulating means  131 . Servo motors  130  are arranged in such a way as to cause animation of various features of the toy  127  such as mouth, eye and ear movements. 
     In addition to the control functions, audio amplifier  124  speaker  125 , and microphone  126  are also operatively connected to sound card  117  which allows the toy  127  to recognize speech, and speak to the human user as part of its interaction capability. 
     The apparatus of the present invention  100  can make use of minutiae analysis  200 , neural networks  300  or another equivalent software algorithm to generate an output signal indicative of verification or non-verification of a human user  150 . 
     There are a variety of methods by which the identification and verification element of the present invention can be implemented. Although the methods differ in computational structure, it is widely accepted that they are functionally equivalent. An example of two practical techniques, minutiae analysis  200  and neural network  300 , each successfully implemented by applicant, are provided herein below and are depicted in  FIG. 2  and  FIG. 3  respectively. 
     As shown in  FIG. 2 , the minutiae analysis  200 , appropriate for implementation of the present invention includes the steps of minutiae detection  210 , minutiae extraction  220  and minutia matching  230 . First, the fingerprint sensor  120  described in detail herein above, digitizes template fingerprint  151  (stored in disk storage device  118  during the enrollment process described further herein below) and target fingerprint  152  from human user  150  and generates local ridge characteristics  211 . The two most prominent local ridge characteristics  211 , called minutiae, are ridge ending  212  and ridge bifurcation  213 . Additional minutiae suitable for inclusion in minutiae analysis  200  of the present invention exist such as “short ridge”, “enclosure”, and “dot” and may also be utilized by the present invention. A ridge ending  212  is defined as the point where a ridge ends abruptly. A ridge bifurcation  213  is defined as the point where a ridge forks or diverges into branch ridges. A fingerprint  151 ,  152  typically contains about 75 to 125 minutiae. The next step in minutiae analysis  200  of the present invention involves identifying and storing the location of the minutiae  212 ,  213  utilizing a minutiae cataloging algorithm  214 . In minutiae cataloging  214 , the local ridge characteristics from step  211  undergo an orientation field estimation  215  in which the orientation field of the input local ridge characteristics  211  acquired by fingerprint sensor  120  are estimated and a region of interest  216  is identified. At this time, individual minutiae  212 ,  213  are located, and an X and Y coordinate vector representing the position of minutiae  212 ,  213  in two dimensional space as well as an orientation angle θ is identified for template minutiae  217  and target minutiae  218 . Each are stored  219  in random access memory (RAM)  114 . 
     Next, minutiae extraction  220  is performed for each detected minutiae previously stored in step  219  above. Each of the stored minutiae  219  are analyzed by a minutiae identification algorithm  221  to determine if the detected minutiae  219  are one of a ridge ending  212  or ridge bifurcation  213 . The matching-pattern vectors which are used for alignment in the minutiae matching  230  step, are represented as two-dimensional discrete signals which are normalized by the average inter-ridge distance. A matching-pattern generator  222  is employed to produce standardized vector patterns for comparison. The net result of the matching-pattern generator  222  are minutiae matching patterns  223  and  224 . With respect to providing verification of a fingerprint as required by the present invention, minutiae template pattern  223  is produced for the enrolled fingerprint  151  of human user  150  and minutiae target pattern  224  is produced for the real-time fingerprint  152  of human user  150 . 
     Subsequent minutiae extraction  220 , the minutiae matching  230  algorithm determines whether or not two minutiae matching patterns  223 ,  224  are from the same finger of said human user  150 . A similarity metric between two minutiae matching patterns  223 ,  224  is defined and a thresholding  238  on the similarity value is performed. By representing minutiae matching patterns  223 ,  224  as two-dimensional “elastic” point patterns, the minutiae matching  230  may be accomplished by “elastic” point pattern matching, as is understood by anyone of ordinary skill in the art, as long as it can automatically establish minutiae correspondences in the presence of translation, rotation and deformations, and detect spurious minutiae and missing minutiae. An alignment-based “elastic” vector matching algorithm  231  which is capable of finding the correspondences between minutiae without resorting to an exhaustive search is utilized to compare minutiae template pattern  223 , with minutiae target pattern  224 . The alignment-based “elastic” matching algorithm  231  decomposes the minutiae matching into three stages: (1) An alignment stage  232 , where transformations such as translation, rotation and scaling between a template pattern  223  and target pattern  224  are estimated and the target pattern  224  is aligned with the template pattern  223  according to the estimated parameters; (2) a conversion stage  233 , where both the template pattern  223  and the target pattern  224  are converted to vectors  234  and  235  respectively in the polar coordinate system; and (3) An “elastic” vector matching algorithm  236  is utilized to match the resulting vectors  234 ,  235  wherein the normalized number of corresponding minutiae pairs  237  is reported. Upon completion of the alignment-based “elastic” matching  231 , a thresholding  238  is thereafter accomplished. In the event the number of corresponding minutiae pairs  237  is less than the threshold  238 , a signal indicative of non-verification is generated by computer  113 . Conversely, in the event the number of corresponding minutiae pairs  237  is greater than the threshold  238 , a signal indicative of verification is generated by computer  113 . Either signal can be utilized to produce a control signal which is communicated by computer  113  to interface electronics  119  via communication cable  121  as described in detail herein above. 
     Referring now particularly to  FIG. 3 , and according to a second preferred embodiment, an exemplary neural network  300  of the present invention includes at least one layer of trained neuron-like units, and preferably at least three layers. The neural network  300  includes input layer  370 , hidden layer  372 , and output layer  374 . Each of the input layer  370 , hidden layer  372 , and output layer  374  include a plurality of trained neuron-like units  376 ,  378  and  380 , respectively. 
     Neuron-like units  376  can be in the form of software or hardware. The neuron-like units  376  of the input layer  370  include a receiving channel for receiving digitized human fingerprint data  152 , and stored comparison fingerprint data  151  wherein the receiving channel includes a predetermined modulator  375  for modulating the signal. 
     The neuron-like units  378  of the hidden layer  372  are individually receptively connected to each of the units  376  of the input layer  370 . Each connection includes a predetermined modulator  377  for modulating each connection between the input layer  370  and the hidden layer  372 . 
     The neuron-like units  380  of the output layer  374  are individually receptively connected to each of the units  378  of the hidden layer  372 . Each connection includes a predetermined modulator  379  for modulating each connection between the hidden layer  372  and the output layer  374 . Each unit  380  of said output layer  374  includes an outgoing channel for transmitting the output signal. 
     Each neuron-like unit  376 ,  378 ,  380  includes a dendrite-like unit  360 , and preferably several, for receiving incoming signals. Each dendrite-like unit  360  includes a particular modulator  375 ,  377 ,  379  which modulates the amount of weight which is to be given to the particular characteristic sensed as described below. In the dendrite-like unit  360 , the modulator  375 ,  377 ,  379  modulates the incoming signal and subsequently transmits a modified signal  362 . For software, the dendrite-like unit  360  comprises an input variable X a  and a weight value W a  wherein the connection strength is modified by multiplying the variables together. For hardware, the dendrite-like unit  360  can be a wire, optical or electrical transducer having a chemically, optically or electrically modified resistor therein. 
     Each neuron-like unit  376 ,  378 ,  380  includes a soma-like unit  363  which has a threshold barrier defined therein for the particular characteristic sensed. When the soma-like unit  363  receives the modified signal  362 , this signal must overcome the threshold barrier whereupon a resulting signal is formed. The soma-like unit  363  combines all resulting signals  362  and equates the combination to an output signal  364  indicative of one of a recognition or non-recognition of a human user  150 . 
     For software, the soma-like unit  363  is represented by the sum α=Σ a X a W a −β, where β is the threshold barrier. This sum is employed in a Nonlinear Transfer Function (NTF) as defined below. For hardware, the soma-like unit  363  includes a wire having a resistor; the wires terminating in a common point which feeds into an operational amplifier having a nonlinear component which can be a semiconductor, diode, or transistor. 
     The neuron-like unit  376 ,  378 ,  380  includes an axon-like unit  365  through which the output signal travels, and also includes at least one bouton-like unit  366 , and preferably several, which receive the output signal from the axon-like unit  365 . Bouton/dendrite linkages connect the input layer  370  to the hidden layer  372  and the hidden layer  372  to the output layer  374 . For software, the axon-like unit  365  is a variable which is set equal to the value obtained through the NTF and the bouton-like unit  366  is a function which assigns such value to a dendrite-like unit  360  of the adjacent layer. For hardware, the axon-like unit  365  and bouton-like unit  366  can be a wire, an optical or electrical transmitter. 
     The modulators  375 ,  377 ,  379  which interconnect each of the layers of neurons  370 ,  372 ,  374  to their respective inputs determines the classification paradigm to be employed by the neural network  300 . Digitized human fingerprint data  152 , and stored comparison fingerprint data  151  are provided as discrete inputs to the neural network and the neural network then compares and generates an output signal in response thereto which is one of recognition or non-recognition of the human user  150 . 
     It is not exactly understood what weight is to be given to characteristics which are modified by the modulators of the neural network, as these modulators are derived through a training process defined below. 
     The training process is the initial process which the neural network must undergo in order to obtain and assign appropriate weight values for each modulator. Initially, the modulators  375 ,  377 ,  379  and the threshold barrier are assigned small random non-zero values. The modulators can each be assigned the same value but the neural network&#39;s learning rate is best maximized if random values are chosen. Digital human fingerprint data  151  and stored comparison fingerprint data  152  are fed in parallel into the dendrite-like units of the input layer (one dendrite connecting to each pixel in fingerprint data  151  and  152 ) and the output observed. 
     The Nonlinear Transfer Function (NTF) employs α in the following equation to arrive at the output:
 
NTF=1/[1 +e   −α ]
 
For example, in order to determine the amount weight to be given to each modulator for any given human fingerprint, the NTF is employed as follows:
 
     If the NTF approaches 1, the soma-like unit produces an output signal indicating recognition. If the NTF approaches 0, the soma-like unit produces an output signal indicating non-recognition. 
     If the output signal clearly conflicts with the known empirical output signal, an error occurs. The weight values of each modulator are adjusted using the following formulas so that the input data produces the desired empirical output signal. 
     For the output layer:
     W* kol =W kol +GE k Z kos      W* kol =new weight value for neuron-like unit k of the outer layer.   W kol =current weight value for neuron-like unit k of the outer layer.   G=gain factor   Z kos =actual output signal of neuron-like unit k of output layer.   D kos =desired output signal of neuron-like unit k of output layer.   E k =Z kos (1−Z kos )(D kos −Z kos ), (this is an error term corresponding to neuron-like unit k of outer layer).   

     For the hidden layer:
     W* jhl =W jhl +GE j Y jos      W* jhl =new weight value for neuron-like unit j of the hidden layer.   W jhl =current weight value for neuron-like unit j of the hidden layer.   G=gain factor   Y jos =actual output signal of neuron-like unit j of hidden layer.   E j =Y jos (1−Y jos )Σ k (E k *W kol ), (this is an error term corresponding to neuron-like unit j of hidden layer over all k units).   

     For the input layer:
     W* iil =W iil +GE i X ios      W* iil =new weight value for neuron-like unit I of input layer.   W iil =current weight value for neuron-like unit I of input layer.   G=gain factor   X ios =actual output signal of neuron-like unit I of input layer.   E i =X ios (1−X ios )Σ j (E j *W jhl ), (this is an error term corresponding to neuron-like unit input layer over all j units).   

     The training process consists of entering new (or the same) exemplar data into neural network  300  and observing the output signal with respect to a known empirical output signal. If the output is in error with what the known empirical output signal should be, the weights are adjusted in the manner described above. This iterative process is repeated until the output signals are substantially in accordance with the desired (empirical) output signal, then the weight of the modulators are fixed. 
     Upon fixing the weights of the modulators, predetermined fingerprint-space memory indicative of recognition and non-recognition are established. The neural network  300  is then trained and can make generalized comparisons of human fingerprint input data by projecting said input data into fingerprint-space memory which most closely corresponds to that data. It is important to note that the neural network  300  described herein above is sensitive to scale, rotation and translation of the input fingerprint patterns. Therefore, preprocessing steps such as those described in detail herein above as employed by minutiae analysis  200  of the present invention should be utilized prior to presenting the fingerprint patterns to the neural network  300 . 
     The description provided for neural network  300  as utilized in the present invention  100  is but one technique by which a neural network algorithm can be employed. It will be readily apparent to those who are of ordinary skill in the art that numerous neural network paradigms including multiple (sub-optimized) networks as well as numerous training techniques can be employed to obtain equivalent results to the method as described herein above. 
     The preferred method of registering and subsequently identifying a human user  150 , of the present invention  100  begins with the human user  150 , enrolling an authorized fingerprint(s) from one or more fingers to be utilized as a template(s) for all subsequent verifications. To accomplish this, the human user  150  enters personal information such as name, nickname, age, sex, and an optional PIN number for example, into computer  113  whereupon said information is stored in a user file on fixed disk  118  and in so doing initiates the enrollment process. The computer  113  subsequently acquires several digitized first human fingerprints of the human user  150  through the use of fingerprint sensor  120  embedded in toy  127 . These first human fingerprints are processed, the highest quality fingerprint(s) selected and thenceforth encoded and stored in the fixed disk  118  of computer  113 . This remaining first human fingerprint will be utilized thereafter as an authorized template fingerprint(s)  151 . The above described process can be repeated if the user wishes to enroll additional fingerprints from other fingers on the user&#39;s hand. Typically, for this application four template fingerprints  151  are sufficient for reliable recognition of human user  150 . In addition, other human users, such as family members and friends, can be enrolled by utilizing a process similar to that described for human user  150  herein above. 
     With respect to Internet access control of the present invention  100 , the enrollment process described herein above is utilized for each authorized user  150  and is further controlled by a system administrator who is also an authorized human user  150 . The system administrator would be responsible for providing additional information for each user pertaining to the Internet web sites each of said authorized human users  150  would be allowed to visit. In this way, the administrator, which could be a parent or guardian, can individually control what Internet access is granted for each of said other human users  150 . The toy  127 , upon recognizing each individual human user, would only permit the user to visit the web sites which were previously identified by the system administrator. Each of said human users  150  would be unable to change which sites could be visited without the permission of the system administrator. 
     Once the human user(s)  150  have been enrolled as described in detail herein above, the toy  127  enters the identification mode wherein it is capable of recognizing a human user  150 . There are myriad applications for toy  127 , which can make use of the capability of recognizing a human user  150 . These applications include various games, educational and interactive software, and the ability to protect users, and more particularly children, from inappropriate Internet web site content. In addition, the toy could provide biometric security for Internet access including protecting the privacy of electronic correspondence (email). 
     When a human user  150  selects a program stored in computer  113  for interacting with the toy  127 , the human user  150  will be instructed to touch the fingerprint sensor  120  embedded in toy  127  and thus triggering a verification event. Once human user  150  touches fingerprint sensor  120  with one of the fingers or thumb previously enrolled as described in detail herein above, fingerprint sensor  120  begins acquiring second human fingerprints of the human user  150  and converts said second human fingerprints to digital data which is subsequently transmitted to computer  113  via interface electronics  119 . The digitized second human fingerprint(s) obtained thereafter are stored in the non-volatile RAM memory  114  of computer  113  as target fingerprint(s)  152 . 
     Once the said target fingerprint(s)  152  has been stored in the computer  113 , the verification software  140 , either minutiae analysis  200  or neural network  300 , or another suitable algorithm is employed to perform a comparison between said stored template fingerprint(s)  151  and said stored target fingerprint(s)  152  and produce an output signal in response thereto indicative of recognition or non-recognition of the human user  150 . The output signal is subsequently utilized by the software to generate a control signal which can include animation and articulation control for toy  127 . The control signal is therewith provided to the interface electronics  119  via communications cable  121 . Interface electronics  119  is additionally responsible for interfacing the computer  113  with toy&#39;s  127  control electronics  128  and enabling the transfer of signals thereto. In the event the said target fingerprint(s)  152  of human user  150  is recognized, the software can be designed to provide a variety of control signals to toy  127 , or can utilize the recognition signal internally as would be the case in controlling Internet web site access. In the event the said target fingerprint(s)  152  of human user  150  is not recognized, the software can be disabled thus preventing access to the program, game or Internet by an unrecognized and unauthorized human user. In addition, in the event target fingerprint(s)  152  of human user  150  is not recognized, the apparatus  100  can optionally notify an authorized system administrator in the event the non-recognition signal is erroneous and a product of a software fault. 
     The above described embodiments are set forth by way of example and are not for the purpose of limiting the claims of the present invention. It will be readily apparent to those or ordinary skill in the art that obvious modifications, derivations and variations can be made to the embodiments without departing from the scope of the invention. For example, the fingerprint verification engine described above as either minutiae analysis or neural network could also be one of a statistical based system, template or pattern matching, or even rudimentary feature matching. Accordingly, the claims appended hereto should be read in their full scope including any such modifications, derivations and variations.

Technology Classification (CPC): 6