Patent Publication Number: US-9432366-B2

Title: Fingerprint based smartphone user verification

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of a prior co-pending U.S. Provisional Patent Application Ser. No. 61/807,113 filed Apr. 1, 2013 entitled “Fingerprint Based Smart Phone User Verification”, the entire contents of which are hereby incorporated by reference. 
    
    
     BACKGROUND 
     1. Technical Field 
     This application relates in general to providing security in mobile electronic devices and in particular to techniques that verify authorized users via touch sensors. 
     2. Background Information 
     For some time it has been common to provide mobile devices such as smartphones, tablet computers, laptop computers and the like with various mechanisms to provide lock-unlock functions. These functions help limit use of the device, to prevent unwanted persons from gaining access. In general a mobile device will perform a lock function when a lock activation mode has been selected by an authorized user. In addition to activation at power on, a mobile device will typically also activate the lock function when there is no input from a user for a period of time. 
     Commonly known factors for controlling lock-unlock are the entry of passwords via a keyboard, finger swipe motions or drawing gestures on a touchscreen, sensors to detect fingerprints, facial recognition via built-in cameras, and others. It is also known to provide further security by combining two or more such factors before granting access to the device. 
     SUMMARY 
     Although there are several existing applications for mobile phones and other devices that can verify a user in order to unlock a phone, entry passwords, facial images, fingerprint “touch ID,” swipe motions or even drawing sequences are a one time entry and do not continually verify the user. This is adequate for some applications, but not necessarily for others, such as a military use, where a device already in use could be taken from a soldier by an enemy. 
     Periodic user verification may be implemented with the same modality as the unlock feature, but at the expense of user productivity. The tradeoff between longer intervals for productivity and shorter intervals for security has no realistic optimum value. Given that these devices are typically used for computing and data communication, and not necessarily voice communication, background voice authentication is not a good omnipresent modality for this assessment. Also, since low power usage is important for field operations, periodic or background image capture for facial image authentication expends a mobile device&#39;s battery prematurely. There are also context and environmental variables such as lighting and uniform or gear changes that affect performance. 
     According to the teachings herein, an active authentication method and system may be based on biometric authentication modalities—“user touchscreen gestures”, which are a biometric behavioral signature in one embodiment, and/or a “finger image”, which is a physiological signature. The same touchscreen sensor data is used for both biometric modalities. These touchscreen sensors are already present in most smartphones, and therefore implementation does not necessarily require retrofitting additional hardware or new types of sensors. 
     The touchscreen, typically implemented as a grid of projected capacitive electrodes, presents an especially effective and transparent method to incorporate active user verification. The preferred solutions work actively as a background process, while the individual interacts and performs their normal work functions with the device. The projected capacitive grid structure can be used to capture enough information to continuously verify that a valid user has possession of the smartphone. As such, there is no need for the user to be actively prompted for authentication data; the user&#39;s natural finger motion itself is used instead. 
     Touch screens use projected capacitive grid structures where every electrode intersection can be unambiguously identified as a touch point. As the user&#39;s finger slides up and down the grid, the ridges and valleys of the finger move across these touch points, superimposing a one dimensional time-based “1-D” profile of the finger “terrain” on the low frequency mutual capacitance effect of the intersecting wires. In one example, there may be four different 1-D profiles simultaneously extracted from the four touch points overlaying the fingerprint. 
     A user&#39;s fingerprints are stored during initialization of the device and then correlated with the 1-D profiles for authentication. 
     Parameters, such as spatial and temporal coupling intervals, can vary considerably between devices. In certain embodiments, these should be about approximately 5 mm and 20 to 200 Hz, respectively. Taking into account the variable speed and location of finger movement by an individual over the touch screen provides an increased spatial and temporal sampling resolution. Therefore adequate data for both the kinematic touch stylometry and finger image can be used as a biometric modality for active user authentication. 
     In some embodiments, the initial authentication or unlock mechanism for the device may be any of the aforementioned factors (passwords, gestures, facial recognition, etc.). The focus here is to instead provide subsequent, active, continuous authentication based on these authentication modalities. 
     Optional aspects of the method and system can be based on previously proven algorithms such as pattern recognition algorithm(s). They can be optionally integrated at a higher level with known Neuromorphic Parallel Processing techniques that have functionality similar to that of the biological neuron, for a multimodal fusion algorithm. For example, 1-D finger profiles may be combined with the outputs from other mobile device sensors such as audio (voice), image, or even user kinematic position stylometry (how the user typically holds the device). This provides additional modalities for authentication without increasing mobile device processing overhead as well as minimizing power consumption. These techniques can be wholly or partially implemented in remote servers accessible via wireless network(s), or in local special purpose neuromorphic procedures. 
     Given the proven robustness of the algorithms, the approach works with a range of spatial sampling resolution of current pro-cap touchscreen devices and the associated temporal sampling rate of the associated processor(s) that perform the algorithms. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The description below refers to the accompanying drawings, of which: 
         FIG. 1  is a block level diagram of the components of a typical smart phone; 
         FIG. 2  is a view of a typical touch sensitive screen; 
         FIG. 3  is a more detailed circuit diagram of a touch sensor; 
         FIG. 4  illustrates touch sensor grid lines superimposed on a typical fingerprint; 
         FIG. 5  is a sequence of operations performed to register and authenticate users using 1-D profiles of a physiological finger biometric attribute sensed with the capacitance grid structure of  FIG. 4 ; 
         FIG. 6  is an example sequence of events for sensing additional 1-D profiles; 
         FIG. 7  is an example authentication decision diagram; 
         FIG. 8  is a typical sparse fingerprint sample used in simulating the detection algorithm; 
         FIG. 9  is a sequence of operations performed to register a user with biometric behavioral or other habitual gesture such as a handwritten signature; 
         FIG. 10  is a typical enrollment screen; 
         FIG. 11  is a typical sequence of operations performed to authenticate a habitual gesture via 1-D profiles taken from the sensor grid of  FIG. 4 ; 
         FIG. 12  is typical authentication decision for a detected habitual gesture; 
         FIG. 13  is an active authentication processing architecture; 
         FIG. 14  is a more detailed view of a neuromorphic parallel processor that may be used to fuze results; and 
         FIG. 15  is a more detailed view of one implementation of a fast neuron emulator. 
     
    
    
     DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS 
     A. Introduction 
     Described below are a system and method for using a touch screen, already present in most mobile devices, to provide active, continuous user authentication. The touch screen inputs provided as a user goes about normal interaction with the device provide sufficient interaction to verify that a valid user has possession of the device. The same physiological data can be combined with habitual gestures detected using the same touchscreen sensors to further authenticate the user. 
     B. Typical Device Architecture 
       FIG. 1  is a high-level block diagram of a typical device in which the methods and systems described herein may be implemented in whole or in part. Those of skill in the art will recognize the block diagram as illustrating example components of a typical smartphone, tablet, laptop computer device  100 , or the like. The device  100  includes a central processing unit (CPU)  102  which may be a integrated circuit microprocessor or microcontroller. CPU  102  includes a read-only memory  103  and random access memory  104 . The CPU  102  also has access to other storage  106 . The CPU executes stored software programs in order to carry out the methods and to provide the system described herein. 
     Also part of the example device  100  are a touchscreen  108  which itself further includes a display portion  109 , a touch sensor portion  110  and touchscreen controller  111 . Additional components of the device  100  may include a keypad  112 , other sensors such as accelerometers  114 , a battery  116 , and a connector  118 . Additional functions and features may include a mobile network communication interface  120 , a local area network communication interface  122 , Bluetooth communication module  124 , camera  126 , Global Positioning System sensor  128  as well as other functions and features not shown in  FIG. 1 . What is important to the present discussion is that the device  100  includes CPU  102  and some sort of touchscreen  108  which can provide output signals to the CPU as described herein. The signals provided by the touchscreen  108  are processed according to the techniques described herein to provide additional security to the device  100  such as by granting or denying access to a user. The techniques described herein may be implemented in low-level device drivers, and/or the kernel of an operating system of the CPU  102 , but may also be implemented at other hierarchical software levels. 
       FIG. 2  is an external view of a typical smartphone device  100 . The device  100  is dominated by the touchscreen  108 . A user  200  is interacting with the touchscreen  108  such as by making one or more gestures  210  on the surface of the touchscreen  108  with their finger. As is known in the art these gestures  210  are detected by a touch sensor  110  and fed to the CPU  102  via controller  111 . 
     A typical touch sensor array is shown in more detail in  FIG. 3 . Such mutual capacitance touchscreens use projected-capacitance (pro-cap) grid structures where every electrode  304  intersection can be unambiguously identified as a touch point. In one example, the electrodes  304 , arranged as grid lines, are transparent direct current (DC) conductors 0.002 inches wide with a grid line spacing of 0.25 inches. This is similar to fingerprint sweep sensors (e.g. Fujitsu MBF300) that also use capacitive sensors, albeit at a higher spatial resolution (500 dpi). In a smartphone  110 , there is typically a protective cover glass lens  302  laminated to the touch screen with a thickness of 0.5 mm. 
     C. Epidermal Finger Pattern Recognition Via Capacitor Sensor Grid 
     As alluded to above, a finger “image” algorithm provides user identification from a sparse data set, sufficiently accurate for continuous user authentication. The projected capacitance touchscreen  108  presents an especially attractive and transparent method to accomplish this active user verification. 
     More particularly, as a user&#39;s finger impedes the proximity of an electrode  304 , the mutual capacitance between electrodes  304  is changed.  FIG. 4  depicts an example fingerprint  400  with the capacitive grid lines  410  overlaid. In this example, the grid lines  410  are superimposed on a fingerprint  400  at four grid intersections, creating four data collection points  420 . The fingerprint ridges  430  are approximately 0.5 mm wide. As the user&#39;s finger  200  slides up, down, and across the grid  410  during normal interaction with the smartphone (using application software and other functions not necessarily related to user authentication processes), the ridges and valleys of the fingerprint  400  are sensed by the difference in mutual capacitance of a ridge versus a valley in proximity to a grid collection point  420 . This superimposes a one dimensional (1-D) profile in time of the “fingerprint terrain” imposed on the intersecting wires. At any given time, the finger  200  could be traversing several collection points in the grid. Each such collection point adds information to the data set, and the data set grows over time proportional to the amount of touch activity. For example, in  FIG. 4  there are four different profiles simultaneously extracted from the four collection touch points  420 . This can occur continuously, even when the user is not actively or consciously engaged in an authentication input session. 
     In one example, the projected capacitive (pro-cap) touch sensor grid is a series of transparent conductors which are monitored for a capacitance change between one another. This change in capacitance is monitored for a series of iterations, circulating throughout the sensor grid up to for example, 200 cycles per second. This sample rate can be increased further by oversampling in the proximity of the calculated finger location, and skipping the grid sensors away from that location. The sampling function may be performed by a touch controller  111 , such as the co-called PSoC chips available from Cypress Semiconductor. 
     The sensor grid  110  may produce a large change in capacitance with finger distance (height), even though the total capacitance is very low (total capacitance is in the picofarads range), allowing the difference between the ridge and trough on a fingerprint to be significant (measurable SNR). To verify this, a full wave FEM electromagnetic simulation was performed using Ansys HFSS, observing the change in impedance of a conductive grid line in close proximity to simulated human flesh material. The finger was assumed to have a real dielectric constant of 29 and a conductivity of 0.55 S/m. The material was moved from 25 mils (spacing when line sensor is in proximity to fingerprint valley) to 20 mils (distance to fingerprint ridge) from the sensor line, and an appreciable impedance change of 7.2% was observed due to the additional capacitance. 
     It should now be understood that these 1-D profiles represent information about the fingerprint of the user, but are not assembled into an actual visual image of the actual fingerprint as is done in prior fingerprint recognition. The data set instead contains many 1-D “terrain profiles” of the finger in various orientations, collected over time. This sparse data set is then correlated to a previous enrollment of the user. Data collected by the grid of sensors is compared (such as by using the techniques further described below or other correlation algorithm) to a database of previously authorized, enrolled users. 
       FIG. 5  is a sequence of steps that may be performed by the CPU  102  to implement active user verification using the 1-D profile spare data sets. In a first state  502  the process starts. In a next state  504  the device  100  may execute an unlock process. This may be by any of the known techniques such as a finger swipe or other gesture, entering a password, facial recognition or other technique. 
     At this point the user is initially known to be authorized to access the device  100  and a registration state  506  may be subsequently entered. From this state  506  one or more 1-D profile data sets are taken from the user. These samples are indicative of the user&#39;s fingerprint profile and will be used in later continuous authentication. One or more of the 1-D profiles are taken for example by collecting data from the four data collection points  420  discussed above, while the user is prompted to interact with the touchscreen. The data taken from the four collection points  420  is then stored as a set of 1-D profiles. One or more of these 1-D profiles may be taken in this registration mode. 
     Registration mode then ends and the CPU then proceeds to allow the user to perform other functions such as normal activities that the user would perform with their device  100 . For example the user may execute application programs, games, make telephone calls, interact with the devices&#39; operating system, and the like all interacting via the touchscreen. During this “normal activity” state a number of continuous authentication steps are taken preferably via a background process. In particular, 1-D profiles are taken in state  512  from the same set of touch points  420  as used for registration. In state  514  these are then compared against the previously stored 1-D profiles. If in, state  516 , there is a match, then the user is retained in the authorized state  518  and processing can then proceed. The user thus has been verified as being an authorized user. However, if in state  416  there is not a sufficient match, a state  518  may be entered with the user no longer being authorized. This state may be entered only after only a single mismatch, or may be entered only after several mismatches are seen. From state  518 , since the current user of the device has been detected as not being authorized, the device may enter a lock mode  520  which shuts down or otherwise stops further access in state  522 . 
       FIG. 6  shows a sequence of optional steps which the system may perform when the user authorized state  518  is active. For example, the system may take additional 1-D profiles. In state  610  these additional profiles may be used not just for continuous further authentication of the user but may be stored in the memory that is added to the 1-D profile database. These additional samples are then used for further matching in state  620  as further described below. 
     In order to assess the viability of the method with these expected sparse data sets, a set of previously obtained 1-D profiles were sampled via simulation and data input into a C-code model.  FIG. 7  shows the resulting signal to noise ratio (related to a correlation factor by Equation (1) below) for a set of valid users and invalid users. 
     
       
         
           
             
               
                 
                   
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     The results were a signal to noise (SNR) output when a sparse piece of fingerprint information was compared to a database of forty (40) National Institute of Standards and Technology (NIST) fingerprints. Half were considered authorized users, and the other half non-authorized. The sparse fingerprint information used in this example was a 0.1 inch wide strip of a fingerprint image as shown in  FIG. 8 . 
       FIG. 7  shows the results of running the 40 fingerprints through the verification simulation. The distributions for the valid users and the invalid users are separated such that both false acceptance and false rejection rates is expected to be low. The low end tail of the valid user distribution is caused by three finger prints known to be smudged in the test group. While the simulation results were accomplished using the two-dimensional strip of  FIG. 8  the actual profile generated by the touch points can be a set of 1-D profile measurements. 
     In fact there would be a multitude of 1-D profiles generated by the touch screen, available to be fused together, using the techniques discussed further below. As per  FIG. 6 , the number of these 1-D profiles will grow as the user continues to use the device by several dozen per second, depending on rate of touch. Each additional data set may be used to increase the confidence of identification when fused together. 
     D. Habitual Gesture (Kinematic) Recognition 
     An active kinematic gesture authentication algorithm may also use the same 1-D profile data sets derived from the same touchscreen  108  sensors. It is designed to derive general biometric motion and compensates for variability in rate, direction, scale and rotation. It can be applied to any time series set of motion detected by the capacitive grid. The preferred implementation is intended for personal signature authentication using the repeated swiping motions on the touchscreen. Touchscreen gestures provide point samples for position, rate, pressure (spot size) and amplitude samples from each sensor point  420  within the spot size. A kinematic authentication algorithm then compares these and other features against known user characteristics and provides a probability of error. 
       FIG. 9  shows a signature registration process. From a start state  902  the device  110  may next perform an unlock sequence  904 . Eventually a state  906  is reached in which the user is known to be authorized. State  908  is then entered in which a registration process proceeds. The user may be presented with a screen, such as that shown in  FIG. 10 , where the user is prompted to perform a kinematic gesture, preferably a habitual gesture such as a signature. In state  910 , 1-D profiles from the sensor array are sampled and stored of this kinematic gesture. The samples are then processed to determine direction, magnitude pressure and potentially other attributes of the habitual gesture in state  912 . In state  914  this information is then stored as that user&#39;s genuine signature profile. 
     A functional block diagram of the companion kinematic authentication algorithm is shown in  FIG. 11 . The input to the algorithm includes two (2) or more reference time series point sets (stored as the genuine signatures in state  914 ) and an unknown series detected from a present user. The algorithm uses raw reference data sets, and does not require training. The algorithm performs compensation for scaling and rotation on each of the point sets, and then compares the individual reference sets to the unknown producing an error value for each. The errors are combined into a single value which is compared to a standard deviation threshold for the known references, which produces a true/false match. 
       FIG. 11  shows one example method for kinematic signature feature extraction, normalization and comparison for use as biometric authentication (it will be understood that others are possible). 
     As shown in  FIG. 11 , a state  1110  is entered in which authentication of a current user of the device  110  is desired using the habitual gesture (kinematic) algorithm. This may be as part of an unlock sequence or some other state where authentication is needed. A next step  1111  is entered in which samples of the 1-D profiles are obtained per the techniques already described above. The 1-D profiles are then submitted to direction  1112 , magnitude  1114 , and pressure  1116  processing. 
     More particularly, step  1111  extracts features from the set of biometric point measurements. The direction component is isolated at state  1112  from each successive pair of points by using the arctangent of deltaX and deltaY resulting in a value within the range of −PI to +PI. This results in the direction component being normalized  1122  to within a range of 2*PI. 
     The magnitude component is extracted in state  1114  by computing the Euclidian distance of deltaX, deltaY and dividing by the sample rate to normalize it at state  1126 . There may be other measurement values associated with each point such as pressure  1116 , which is also extracted and normalized  1126 . 
     The set of extracted, normalized feature values are then input to a comparison algorithm such as Dynamic Time Warping (DTW) or Hidden Markov Model for matching ( 1132 ,  1134 ,  1136 ) against a set of known genuine patterns  1130  for identification. 
     For signature verification, the normalized points are derived from a set of library data sets which are compared to another normalized set to determine a genuine set from a forgery. The purpose of normalization  1112 ,  1114 ,  1116  is to standardize the biometric signature data point comparison. Prior to normalization, the features are extracted from each pair of successive x, y points for magnitude  1114  and direction  1112 . The magnitude value may be normalized as a fraction between 0.0 to 1.0 using the range of maximum and minimum as a denominator. The direction value may be computed as an arctangent in radians which is then normalized between 0.0 to 1.0. Other variations may include normalization of the swipe dynamics such as angle and pressure. The second order values for rate and direction may also be computed and normalized. The first order direction component isolates from scaling. A second order direction component will make it possible to make the data independent of orientation and rotation. 
     To verify, several genuine signatures are preferably used as a ‘gold standard’ reference set. First, the genuine reference set is input, extracted and normalized. Then each unknown scan is input, extracted and normalized and compared point by point against each signature in the genuine reference set. 
     To perform the signature pair comparison, a DTW N×M matrix may be generated by using the absolute difference between each corresponding point from the reference and one point from the unknown. The matrix starts at a lower left corner (0,0) and ends at the upper right corner. Once the DTW matrix is computed, a backtrace can be performed starting at the matrix upper right corner position and back-following the lowest value at each adjacent position (left, down or diagonal). Each back-position represents the index of matching position pairs in the two original point sets. The average of the absolute differences of each matching position pair is computed using the weighted recombination of the normalized features. This is a single value indicating a score  1140  as an aggregate amount of error between the signature pairs. 
     The range of each error score is analyzed and a precomputed threshold  1142  is used to determine the probability of an unknown signature being either a genuine or an outlier. The threshold value is determined by computing error values of genuine signatures against a mixed set of genuine signatures and forgeries. The error values are used to determine a receiver operating characteristic (ROC) curve which represents a probability of acceptance or rejection. 
     The kinematic algorithm was implemented in a functional online demonstration. Signature collection was performed on an Apple™ iPad and interfaced to a server which contained the reference signatures and the authentication algorithm. In this set-up, signatures of several data bases were used with the most significant being the test data set from SigComp2011. Each individual supplied 24 genuine reference signatures with several skilled forger individuals providing an equal number or more of forgeries. The probability distribution for the set of Chinese signatures from person 001 (ChineseSet001) which has 24 genuine signatures and 36 forgeries, is depicted in  FIG. 12 . The peak on the left represents genuine signatures, and the peaks on the right represents forgeries. Excellent separation exists between distributions. The average Equal Error Rate (EER) for all genuine signatories was 1% using 14 reference signatures and 2.4% using only 2 reference signatures. This implies a better accuracy than the winner of the SigComp2011 competition who averaged greater than 3% on both False Accept and Reject (FAR/FRR). 
     E. Combining Epidermal and Kinematic Recognition/Fusion 
     In this approach, we authenticate a user by exploiting both their (1) habitual touchscreen gestures (as per  FIG. 11 ) along with (2) the epidermal characteristics of their finger or “finger image” (as per  FIG. 4 ). 
     The kinematic touchscreen gesture authentication algorithm exploits the biometric modality of habitual human motion in order to verify an individual who has previously registered their personal data movements. This modality is not in wide use and is extremely difficult to imitate because it contains motion timing that can only be replicated by skillful and intense observation. 
     In one implementation, the sparse resolution sampling of a projected capacitive touch screen can be used to uniquely identify a registered user from the 1-D profiles collected via the pro-cap sensor grid  110 . As one example, the Neuromorphic Parallel Processing technology, such as that described in U.S. Pat. No. 8,401,297 incorporated by reference herein, may be used. Processing may be distributed at a network server level to fuse these different biometric modalities and provide another level of authentication fidelity to improve system performance. The aforementioned Neuromorphic Parallel Processor technology for multimodal fusion, specifically the fast neural emulator, can also be a hardware building block for a neuromorphic-based processor system. These mixed-mode analog/digital processors are fast neural emulators which convolve the synaptic weights with sensory data from the first layer, the image processor layer, to provide macro level neuron functionality. The fast neural emulator creates virtual neurons that enable unlimited connectivity and reprogrammability from one layer to another. The synaptic weights are stored in memory and output spikes are routed between layers. 
     The preferred architecture follows the process flow of the active authentication application as per  FIG. 13 . A server application  1310  continues to validate the user—but resides as part of a network including the devices  110  at a server. This is where the higher layers of the neuromorphic processor can reside. The mobile platform  110  fuses touchscreen movement and finger 1-D profile data and provides  1310  an evaluation of the level of confidence using local pattern recognition algorithms, as described above. 
     Processing, identification and validation functionality  1310  may reside on the mobile platform  110  as much as possible. In order to accommodate potential commercial mobile platform microprocessor and memory constraints, a more flexible architecture is to allow the entire chain of pattern recognition and active authentication to be accomplished by the mobile device as shown in  FIG. 13 . The mobile device acquires touchscreen data from the pro-cap controller  111 , reduces data for feature extraction, and provides the applicable 1-D profile data sets of the gesture and finger image for classification, recognition and authentication to the server. This architecture also minimizes the security level of software in the mobile platform. 
     A functional block diagram of a stand alone neuromorphic processor which is optionally added to the device  110  and/or server is shown in  FIG. 14 . It has five (5) function layers. The processor contains components that are part of the first three layers. The first  1410  of these layers is an “image” processor. The second layer  1412  is populated with feature based representations of the 1-D profile objects such as finger ‘images’ or touchscreen habitual gesture, and is not unlike a universal dictionary of features. Here, the term ‘images’ is used to describe the multi-dimensional data set of 1-D profiles. The third layer  1414  is the object class recognizer layer, while the fourth and fifth layers are concerned with inferring the presence of situations of interest. 
     The design implementation of a five (5) layered neuromorphic parallel processor solution addresses the need for a low-power processor that can facilitate massive computational resources necessary for tasks such as scene understanding and comprehension. It is similar to that of a biological neuron with its mixed-mode analog/digital fast neural emulator processor capability where some key features are: Low Size, Weight and Power (SWaP), Low Loss, and Low Installation Complexity and Cost. 
     One building block of the neuromorphic parallel processor can be a fast neuron emulator shown in  FIG. 15 . A convolution function is implemented by means of a chirp Fourier transform (CFT) where the matched chirp function is superimposed on the synaptic weights, which are convolved with the incoming data and fed into the dispersive delay line (DDL). If the synaptic weights are matched to the incoming data, then a compressed pulse is seen at the output of the dispersive delay line similar to the action potential in the neural axon. An executive function may control multiple (such as four (4)) fast neuron emulators  1500 . The feature based representations are reduced dimensionality single bit complex representations of the original data. 
     The feature based representations of objects in the second layer  1414  of the neuromorphic parallel processor may be fused to obtain better performance when recognition of individual objects is the objective. Fusion of multimodal biometric data to achieve high confidence biometric recognition is used to illustrate the algorithm. 
     A biometric system can thus be divided into three (3) stages— 
     1. Feature extraction, in which the biometric signature is determined, 
     2. Matching, in which the degree of match between an unknown signature and an enrolled signature is determined, and 
     3. Decision, in which the determination of whether or not a match is made. 
     Our preferred approach is based on fusion at the matching stage. In this approach, separate feature extraction is performed on each biometric input and a score is independently developed regarding the confidence level that the extracted signature for each modality matches a particular stored (e.g., authenticated) biometric record. Then a statistical combination of separate modal scores is done based on the scores and the known degree of correlation between the biometric modalities. 
     The scores are weighted by the source data quality in both the enrollment and the captured image to give preference to higher quality capture data. If the modes are completely independent (such as habitual gesture and fingerprint terrain) the correlation is near zero and the mode scores are orthogonal resulting in maximum information in the combined score. If there is a correlation between the modes, the scores are not completely orthogonal, but neither are they coincident, allowing additional confidence information to be extracted from the orthogonal component.