Abstract:
Techniques are described for authenticating security documents having security images that incorporate multiple security features. The techniques may produce robust template matching results in situations of lighting unevenness, even with stronger occluding objects. The techniques may be particularly useful in validating a security document having a security image composed of one or more “virtual” retroreflective images formed over a background of a repeating retroreflective image. The virtual retroreflective images within the security image provide strong signals that may dominate analysis and validation of the background retroreflective image, thereby resulting incorrect authentication. The techniques provide a modified normalized cross-correlation analysis that masks out the strong signals contributed by the additional security features, such as the retroreflective virtual images, while still allowing for fast and efficient template matching to be performed with respect to the background image.

Description:
TECHNICAL FIELD 
       [0001]    The invention relates to computer-aided authentication of security documents, such as passports, driver&#39;s licenses, birth certificates, or financial documents, based on image pattern confirmation. 
       BACKGROUND 
       [0002]    Computer-aided techniques are increasingly being used to validate the authenticity of security documents. Examples of security documents include passports, credit cards, ID cards, driver&#39;s licenses, birth certificates, commercial paper and financial documents. In some cases, security images are printed on, embossed in, engraved in, or affixed to the security documents to aid detection of counterfeiting or use of unauthorized documents generally. That is, the security images can include one or more security features, such as ultraviolet ink, retroreflective 3M Confirm™ images, and retroreflective virtual images that are difficult to reproduce and, therefore, lead to more reliable validation of the security documents and increased detection of unauthorized documents. 
       SUMMARY 
       [0003]    In general, the invention relates to techniques for authenticating security documents having security images that incorporate multiple security features. The common situation in image template matching techniques is to detect a strong signal pattern from a quiet background, or with weak noise interference. Occlusion caused by other security features complicates the confirmation of an expected image pattern, especially when the occluding objects have a stronger signal than the image pattern. The techniques may be particularly useful in validating a security document having a security image composed of one or more “virtual” retroreflective images formed over a background of a repeating retroreflective confirm image. 
         [0004]    As further described herein, the virtual retroreflective images within the security image provide stronger signals that may dominate analysis and validation of the background retroreflective confirm image, thereby resulting in incorrect authentication. The techniques provide a modified normalized cross-correlation analysis that masks out the strong signals contributed by the additional security features, such as the one or more retroreflective virtual images, while still allowing for fast and efficient template matching to be performed with respect to the background image. 
         [0005]    In one embodiment, a method comprises capturing an image of at least a portion of a security document, wherein the portion of the security document includes a security image having a first retroreflective feature and a second retroreflective feature, calculating a modified normalized cross-correlation between a reference image associated with the first retroreflective feature and the captured image using a masking image generated from the captured image, and outputting indicia of one or more matches between the reference image and the captured image. 
         [0006]    In another embodiment, a security document authentication device comprises an image capture interface to receive a captured image of at least a portion of an article, wherein the portion of the article includes a security image having a first retroreflective feature and a second retroreflective feature. The authentication device also includes a fast masked normalized cross-correlation (FMNCC) module to calculate a modified normalized cross-correlation between a reference image associated with the first retroreflective feature and the captured image using a masking image generated from the captured image, and a display to output indicia of one or more matches between the reference image and the captured image. 
         [0007]    In another embodiment, a security document authentication system comprises an image capture device that captures at least a portion of an image of an article, wherein the portion of the article includes a security image having a first retroreflective feature and a second retroreflective feature, and a host system that calculates a modified normalized cross-correlation between a reference image associated with the first retroreflective feature and the captured image using a masking image generated from the captured image, and outputs indicia of one or more matches between the reference image and the captured image. 
         [0008]    In yet another embodiment, a method comprises capturing an image of at least a portion of a document, wherein the portion of the document includes an image having a first feature and a second feature. The method further includes calculating a modified normalized cross-correlation between a reference image associated with the first feature and the captured image using a masking image generated based on the captured image, and outputting indicia of one or more matches between the reference image and the captured image. 
         [0009]    The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. 
     
     
       BRIEF DESCRIPTION OF DRAWINGS 
         [0010]      FIG. 1  is a schematic representation illustrating an exemplary document authentication system that analyzes security documents in accordance with the principles of the invention. 
           [0011]      FIG. 2  is a block diagram illustrating an exemplary host system that uses a fast masked normalized cross-correlation technique to analyze security documents in accordance with the principles of the invention. 
           [0012]      FIG. 3  is a flowchart illustrating example operation of the document authentication system of  FIG. 1 . 
           [0013]      FIG. 4  is a flowchart illustrating example operation of the host system of  FIG. 2 . 
           [0014]      FIG. 5  is a reproduced image illustrating an example captured image of an exemplary security image on a security document. 
           [0015]      FIG. 6  is a reproduced image illustrating an exemplary reference image. 
           [0016]      FIG. 7  is a reproduction of an example masking image detected from the example captured image of  FIG. 5 . 
           [0017]      FIGS. 8A-8C  show actual results obtained when different template matching techniques are performed to analyze the example captured image of  FIG. 5 , shown for purposes of comparison. 
           [0018]      FIG. 9  illustrates an exemplary display image presented by the host system. 
           [0019]      FIG. 10  is an exemplary output window presented by the host system on a display, illustrating example template matching data. 
       
    
    
     DETAILED DESCRIPTION 
       [0020]      FIG. 1  is a schematic representation illustrating an exemplary document authentication system  10  for analyzing a security document  12  in accordance with the principles of the invention. Security document  12  includes at least one security image  13  that includes one or more security features. Document authentication system  10  may be used to authenticate or validate security document  12 , such as by verifying that security document  12  possesses certain security features. The security features may be, for example, retroreflective images, retroreflective virtual images, or other types of images printed on, embossed in, or affixed to security document  12 . 
         [0021]    Security document  12  may be, for example, a passport, driver&#39;s license, identification card, title document, or other article. Document authentication system  10  may be used to verify the authenticity of the passport by determining whether security image  13  contains a retroreflective image that matches a stored reference retroreflective image, also referred to as a “template” image. Document authentication system  10  may perform this determination by capturing an image of the security image  13  or of the entire face of the security document  12 , and analyzing the captured image to determine whether one or more occurrences of the reference image are present within the captured image. If the reference image is present within the captured image, document authentication system  10  provides an indication (e.g., audible and or visual) that security document  12  has been properly verified. If the reference image is not present within the captured image, document authentication system  10  provides an indication that security document  12  cannot be automatically verified and may be denied. 
         [0022]    In some embodiments security image  13  may include additional security features. For example, in addition to containing one or more image regions matching the reference image, security image  13  may include a retroreflective virtual image (e.g., a “floating” image) as an additional security feature. Exemplary techniques for forming a floating image within security image  13  are described in U.S. Pat. No. 6,288,842, entitled “Sheeting With Composite Image that Floats” to Florczak et al., hereby incorporated herein by reference. As another example, security image  13  may include ultraviolet patterns, printed visible patterns, or biometric features such as fingerprints. 
         [0023]    The retroreflective image and other security features contained within security image  13  may render template matching more difficult when using conventional template matching techniques. In particular, the security features, such as the retroreflective virtual image, may produce a stronger signal within the captured image relative to the signal representative of the reference image, thereby making it difficult to verify that security image  13  includes an authentic reference image. For this reason, host system  20  of document authentication system  10  uses a “fast masked normalized cross-correlation” (FMNCC) technique described herein to effectively and efficiently mask out any signals present in the captured image that are due to the additional security features. Host system  20  applies the FMNCC technique to the captured image to provide fast template matching analysis and authentication or denial of security document  12 . 
         [0024]    In operation, a user places security document  12  onto view frame  14 . View frame  14  accurately locates security document  12  with respect to other components of document authentication system  10 . In the exemplary embodiment of  FIG. 1 , document authentication system  10  includes a light source  16  to illuminate security document  12  placed onto view frame  14 . Light source  16  may be arranged at an angle relative to a viewing position. In some embodiments, document authentication system  10  may include more than one light source. Document authentication system  10  further includes an image capture device  18  arranged at the viewing position. Image capture device  18  may be a camera, such as a charge coupled device (CCD), a line scanner or other optical input device. Light source  16  and image capture device  18  may be controlled by host system  20 . The intensity of light source  18  may be adjusted through a range of intensities from a minimum value to a maximum value either automatically by host system  20  or based on input from the user. 
         [0025]    After the user has placed security document  12  into view frame  14 , image capture device  18  captures an image of security document  12  that includes security image  13 . The captured image may represent all or a portion of security document  12 , provided the portion includes security image  13 . Image capture device communicates the captured image to host system  20  for image processing via signal path  22 . Captured images processed by host system  20  can be displayed for examination on a display (not shown) associated with host system  20 . Host system  20  may be, for example, a computer, laptop, mobile personal digital assistant (PDA) or other computing system having sufficient processor and memory resources to analyze the captured image. Example configuration and operation of host system  20  are described in further detail below. 
         [0026]      FIG. 2  is a block diagram illustrating an exemplary host system  20  that uses a fast masked normalized cross-correlation technique to analyze security documents in accordance with the principles of the invention. Host system  20  analyzes image data received from image capture device  18  ( FIG. 1 ) to determine whether a given reference image may be found within (i.e., matched to) the captured image. 
         [0027]    Host system  20  includes an image capture interface  24  to receive the image data from image capture device  18  via signal path  22 . Image capture interface  24  may be, for example, a serial or parallel hardware interface for communicating with image capture device  18  and receiving image data. As another example, image capture interface  24  may be a universal serial bus (USB) interface. Host system  20  may store the received image data in image storage  26 , e.g., as one or more files, and may update database  30  to reflect the location of the image data within image storage  26 . Image storage  26  may be an internal or external persistent storage medium, such as a hard disk, optical disk, tape, removable memory card or the like. 
         [0028]    Host system  20  may also receive user input  32  via user interface  34 , and may update database  30  in response to the user input. User input  32  may include, for example, selection of a reference image from a library of reference images, stored as templates  28  within image storage  26 . The library of reference images may include a plurality of reference images of particular security features associated with different types of security documents. The reference images are used to verify the authenticity of a security document, such as a passport, driver&#39;s license, financial document or other security document. 
         [0029]    The image data received by image capture interface  24  via signal path  22  may represent a captured image of all or a portion of security document  12 . As discussed above, the captured image may contain primary security features and additional security features, such as a retroreflective virtual image. Host system  20  calculates a modified normalized cross-correlation between the reference image and the captured image. In particular, masking image generator  36  generates a “masking image” representing the regions of the captured image containing stronger signals, or other obvious image features, due to the additional security features, and applies the masking image to remove the effect of the additional security features from the normalized cross-correlation calculation. 
         [0030]    Masking image generator  36  dynamically builds the masking image in real time, wherein the masking image has the same dimensionality as the portion of the captured image associated with secure image  13 . In one embodiment, masking image generator  36  may build the masking image by thresholding the images of the captured image on a pixel-by-pixel basis to locate regions of high signal strength, i.e., image regions that are very bright or very dark. Masking image generator  36  may also perform some morphology processing. For example, masking image generator  36  may produce the masking image by setting the pixels of the masking image to either a highest brightness value (e.g., 255 for an 8-bit pixel) or a lowest brightness value (e.g., 0) based on whether a brightness value of a corresponding pixel within the captured image falls within or outside a pre-defined range of brightness values. As one example, when a pixel of the captured image has a brightness value outside of a range from 50-200 (i.e., less than 50 or greater than 200), masking image generator  36  may set a corresponding pixel within the masking image to a brightness value of 0 (black); and where the pixel of the captured image has a brightness value that falls within the range of 50 to 200, masking image generator  36  may set the corresponding pixel of the masking image to a brightness value of 255 (white). Several rounds of dilation and erosion could significantly improve the shape of the masking region. The particular range and method used to build the masking image may vary according to a type or characteristics of the security image  13  or the characteristics of the security features contained within the security image. Masking image generator  36  may also include multiple pre-defined ranges of brightness, which vary depending on the type of document being analyzed. The range of brightness values may also be dynamically estimated. Masking image generator  36  may also statistically pre-compute the masking image in advance. 
         [0031]    Masking image generator  36  provides the masking image to fast masked normalized cross-correlation (FMNCC) module  38 . FMNCC module  38  obtains the captured image from image storage  26 , and applies the masking image to the captured image to produce a “masked” image. The masked image may be considered a version of the captured image, but with any strong signals caused by the additional security features, e.g., a retroreflective floating image, being at least in part filtered. FMNCC module  38  calculates and normalizes the cross-correlation between the reference image and the masked image, and compensates the result by multiplying by the ratio of the template size to the masked area at the respective match. Based on the result of these calculations, collectively referred to as one embodiment of FMNCC, host system  20  may determine pixel regions within the captured image that sufficiently match the reference image, referred to as matching regions. 
         [0032]    FMNCC module  38  may find the reference image at a plurality of regions within the masked image. When a match is identified, host system  20  produces a positive indication, e.g., an audible and/or visual indicator of the match. For example, host system  20  may display a visual representation of all or a portion of the captured image and the matching regions within the captured image via display  40 . Alternatively, if FMNCC module  38  finds no sufficient match to the reference image in the masked image, host system  20  outputs a negative indicator (audio or visual) to indicate denial of security document  13 . For example, host system  20  may display a message indicating no matches were found or other suitable error message. 
         [0033]      FIG. 3  is a flowchart illustrating example operation of the document authentication system  10  of  FIG. 1 . Initially, host system  20  stores one or more reference images  28  to image storage  26 . Next, a user places a security document  12 , such as a passport, under image capture device  18  ( 42 ). Host system  20  receives and stores a captured image of the security document  12  using image capture device  18  and light source  16  ( 44 ). 
         [0034]    Host system  20  may identify the type of security document  12  being authenticated manually or automatically using various characteristics. Based on the identification, host system  20  selects one or more reference images for the document type from among templates  28  ( 46 ). For example, the user may manually select the reference image via user interface  34 . Alternatively, host system  20  may employ an automatic document identifier to automatically or semi-automatically select the reference image based on characteristics of the captured image (e.g., document size, machine-readable zone (MRZ), or other text optical character recognition (OCR) or automated recognition of certain pre-defined markings). As another example, host system  20  may use a default reference image for each type of security document  12 . 
         [0035]    After selecting the template, host system  20  performs fast masked normalized cross-correlation (FMNCC) analysis to determine a cross-correlation between the captured image and the reference image ( 48 ). In this manner, host system  20  determines whether one or more instances of the reference image are present within the captured image. This analysis is described in further detail below. Host system  20  may display results of the FMNCC analysis on display  40  or produce any other suitable audio or visual indicator ( 50 ). 
         [0036]      FIG. 4  is a flowchart illustrating example operation of host system  20  of  FIG. 2  in further detail. Host system  20  authenticates security document  12  by analyzing a captured image associated with the security document. As described above, host system  20  receives a captured image of security document  12  via image capture interface  24  ( 52 ), and may store the captured image to image storage  26 . FMNCC module  38  generates a masking image m(x, y) from the captured image, e.g., by thresholding the captured image as described above with respect to  FIG. 2  ( 54 ). In addition, host system  20  accesses a reference image from templates  28  of image storage  26  ( 56 ). 
         [0037]    FMNCC module  38  then uses the masking image to remove undesired objects from a normalized cross-correlation between the reference image and the masking image. More specifically, FMNCC module  38  applies the masking image to the captured image to obtain a masked image f m (x, y) ( 58 ). FMNCC module  38  may do this by introducing the masking image, represented by m(x, y), into a normalized cross-correlation calculation between the reference template image t(x, y) and the captured image f(x, y). The normalized cross-correlation between the template image and the captured image is given by: 
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         [0000]    where  t  is the mean of the template and  f   u,v  is the mean of the captured image f(x, y) in the region under the template image. 
         [0038]    The equation for the normalized cross-correlation γ m (x, y) with the masking image applied onto the captured image is given by: 
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         [0000]    where  t  is the mean of the template and  f   u,v  is the mean of f(x, y) in the region under the template at the position (u, v) and  f   m(u,v)  is the respective mean of the masked image, f m (x, y). 
         [0039]    Rearranging equation (2) results in the following equation: 
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                                
                               
                                 
                                   
                                     m 
                                      
                                     
                                       ( 
                                       
                                         
                                           x 
                                           + 
                                           u 
                                         
                                         , 
                                         
                                           y 
                                           + 
                                           v 
                                         
                                       
                                       ) 
                                     
                                   
                                    
                                   
                                     [ 
                                     
                                       
                                         t 
                                          
                                         
                                           ( 
                                           
                                             x 
                                             , 
                                             y 
                                           
                                           ) 
                                         
                                       
                                       - 
                                       
                                         t 
                                         _ 
                                       
                                     
                                     ] 
                                   
                                 
                                 2 
                               
                             
                             } 
                           
                           0.5 
                         
                       
                        
                       
                         
                           γ 
                           m 
                           ′ 
                         
                          
                         
                           ( 
                           
                             u 
                             , 
                             v 
                           
                           ) 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   3 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where γ′ m (u, v) is the normalized cross-correlation between the template image t(x, y) and the masked image f m (x, y), and γ′ m (u, v) is corrected by a modifier. The denominator of this modifier is in the form of cross-correlation between the masking image and the reference template image. In some instances a fast Fourier transform (FFT) may be used to improve calculation efficiency of a cross-correlation. However, introduction of the masked image complicates the matter by introducing another convolution in the denominator, thereby increasing the overall computational complexity of the cross-correlation as well as making it more difficult to improve calculation efficiency. 
         [0040]    In order to improve the calculation efficiency, it is recognized herein that the denominator of the modifier is the sum of the square of each pixel in the portion of the captured image that is filtered out by the masking image, so this modifier in equation (3) represents the ratio of the sum of the square of each pixel in the reference image to the sum of the square of each pixel in this masked out portion. In many embodiments, the reference image consists of a graphic pattern that has approximately equal lightness throughout. For these embodiments, we can assume that the signal is uniformly present throughout the reference image, i.e., that the signal exists equally in every part of the reference image. By assuming that the signal is uniformly distributed throughout the reference image, we can assume that the variance σ of the whole reference image should be close to the variance σ m  of the masked part. 
         [0041]    Thus, based on this recognition, we can simplify equation (3) to obtain the following equation: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         
                           
                             
                               γ 
                               m 
                             
                              
                             
                               ( 
                               
                                 u 
                                 , 
                                 v 
                               
                               ) 
                             
                           
                           = 
                             
                            
                           
                             
                               
                                 
                                   { 
                                   
                                     
                                       M 
                                       t 
                                     
                                     × 
                                     
                                       N 
                                       t 
                                     
                                   
                                   } 
                                 
                                 × 
                                 σ 
                               
                               
                                 
                                   
                                     { 
                                     
                                       
                                         ∑ 
                                         
                                           x 
                                           , 
                                           y 
                                         
                                       
                                        
                                       
                                         m 
                                          
                                         
                                           ( 
                                           
                                             
                                               x 
                                               + 
                                               u 
                                             
                                             , 
                                             
                                               y 
                                               + 
                                               v 
                                             
                                           
                                           ) 
                                         
                                       
                                     
                                     } 
                                   
                                   0.5 
                                 
                                 × 
                                 
                                   σ 
                                   m 
                                 
                               
                             
                              
                             
                               
                                 γ 
                                 m 
                                 ′ 
                               
                                
                               
                                 ( 
                                 
                                   u 
                                   , 
                                   v 
                                 
                                 ) 
                               
                             
                           
                         
                         , 
                       
                     
                   
                   
                     
                       
                         
                           ≈ 
                             
                            
                           
                             
                               
                                 { 
                                 
                                   
                                     { 
                                     
                                       
                                         M 
                                         t 
                                       
                                       × 
                                       
                                         N 
                                         t 
                                       
                                     
                                     } 
                                   
                                   
                                     
                                       ∑ 
                                       
                                         x 
                                         , 
                                         y 
                                       
                                     
                                      
                                     
                                       m 
                                        
                                       
                                         ( 
                                         
                                           
                                             x 
                                             + 
                                             u 
                                           
                                           , 
                                           
                                             y 
                                             + 
                                             v 
                                           
                                         
                                         ) 
                                       
                                     
                                   
                                 
                                 } 
                               
                               0.5 
                             
                              
                             
                               
                                 γ 
                                 m 
                                 ′ 
                               
                                
                               
                                 ( 
                                 
                                   u 
                                   , 
                                   v 
                                 
                                 ) 
                               
                             
                           
                         
                         , 
                       
                     
                   
                 
               
               
                 
                   
                     
                       
                         ( 
                         4 
                         ) 
                       
                     
                   
                   
                     
                       
                           
                       
                     
                   
                   
                     
                       
                           
                       
                     
                   
                   
                     
                       
                         ( 
                         5 
                         ) 
                       
                     
                   
                 
               
             
           
         
       
     
         [0000]    where σ is the variance of the whole reference image and σ m  is the variance of the masked part, M t  and N t  define the dimensions of the reference image, and γ′ m (u, v) is the normalized cross-correlation of the reference image and the masked image. If we let the modifier, referred to now as the masking effect modifier, be estimated by the ratio of the template size to the masked area at each matching region, equation (5) can be generalized as: 
         [0000]    
       
         
           
             
               
                 
                   
                     
                       
                         γ 
                         m 
                       
                        
                       
                         ( 
                         
                           u 
                           , 
                           v 
                         
                         ) 
                       
                     
                     ≈ 
                     
                       
                         
                           { 
                           
                             
                               { 
                               
                                 
                                   M 
                                   t 
                                 
                                 × 
                                 
                                   N 
                                   t 
                                 
                               
                               } 
                             
                             
                               
                                 ∑ 
                                 
                                   x 
                                   , 
                                   y 
                                 
                               
                                
                               
                                 m 
                                  
                                 
                                   ( 
                                   
                                     
                                       x 
                                       + 
                                       u 
                                     
                                     , 
                                     
                                       y 
                                       + 
                                       v 
                                     
                                   
                                   ) 
                                 
                               
                             
                           
                           } 
                         
                         α 
                       
                        
                       
                         
                           γ 
                           m 
                           ′ 
                         
                          
                         
                           ( 
                           
                             u 
                             , 
                             v 
                           
                           ) 
                         
                       
                     
                   
                   , 
                 
               
               
                 
                   ( 
                   6 
                   ) 
                 
               
             
           
         
       
     
         [0000]    where α represents the modification strength. 
         [0042]    In practice, using a value of α=1 gives a fast and good approximation of γ′ m (u, v), the normalized cross-correlation between the reference image and the masked image. This simplification enables FMNCC module  38  to apply an FFT to improve the efficiency of the calculation. FMNCC module  38  obtains the final result by calculating the cross-correlation between the masked image and the reference image using FFT ( 60 ) and normalizing the cross-correlation ( 62 ) (in other words, calculating γ′ m (u, v)), and modifying the normalized cross-correlation by the masking effect by multiplying γ′ m (u, v) with the masking effect modifier ( 64 ). In certain embodiments, FMNCC module  38  may make this calculation at a speed on the order of hundreds of milliseconds where host system  20  includes a general purpose processor, e.g., a 2 GHz processor. 
         [0043]    Host system  20  determines whether any matching regions exist based on the results of the fast masked normalized cross-correlation analysis described above. In particular, host system utilizes the results of the fast-masked normalized cross-correlation techniques described above to determine whether any regions within the captured image sufficiently match the reference image. Host system  20  may determine whether any matching regions are found within the captured image based on a predefined score threshold. As one example, a predefined score threshold may require a correlation score of at least 75 on a scale of 0 to 100. In some embodiments, host system may determine whether any matching regions are found within the captured image based on a minimum required number of matches having a correlation score above the predefined score threshold. As one example, host system  20  may be configured such that for any matching regions to be found, at least 3 matching regions must have a correlation score above the predefined score threshold. In some example embodiments, different predefined score thresholds may be defined for different document types. 
         [0044]    In some embodiments, host system  20  displays all or a portion of the captured image on display  20 , and identifies any regions therein that sufficiently match the reference image  40  ( 66 ). For example, host system  20  may display the captured image of the security document with the matching regions highlighted. As another example, host system  20  may present a list of matching regions, e.g., a center pixel in the matching region of the captured image that matches the reference image. The matching regions may be shown with a corresponding correlation score, e.g., from 0-100. FMNCC module  38  may identify matching regions even where the matched image is rotated with respect to the reference image. Host system  20  may indicate such rotation when the matches are displayed. Alternatively, host system  20  may simply give a pass/fail indication of security document  12 . 
         [0045]      FIG. 5  is reproduced image illustrating an example captured image  70  of an exemplary security image on a security document. Captured image  70  shows a security image having a plurality of security features. In particular, captured image  70  includes a retroreflective background pattern  72 , which includes multiple instances of a reference image shown in  FIG. 6 , as well as an additional security feature in the form of a retroreflective virtual image  74 . 
         [0046]    As illustrated in  FIG. 5 , the retroreflective background pattern  72  includes a repeated pattern consisting of the word “CONFIRM” and a circular emblem design. A retroreflective sheet material having glass microspheres may be bonded to a security document into which the repeated pattern is formed. The pattern may be retroreflective, such that it is visible when viewed under retroreflected light. The additional security feature of a retroreflective virtual image  74  is formed over the background pattern and, in this example, is a wave design with the symbols “3M” appearing along the wave. Retroreflective virtual image  74  may be a floating image that may appear to move relative to the document. The captured image  70  may be captured by image capture device  18  ( FIG. 1 ) when a security document is placed onto view frame  14 . 
         [0047]      FIG. 6  is a reproduced image illustrating an exemplary reference image  76  from which retroreflective background pattern  72  was created. In this example, reference image  76  includes a retroreflective security emblem. Reference image  76  may be stored in template data structure  28 , and may be manually selected by an operator of host system  20  via user interface  34  ( FIG. 2 ) after the user has viewed captured image  70 , or may be automatically selected by host system  20  by analyzing characteristics of the document. In accordance with the techniques described herein, host system  20  processes captured image  70  to identify one or more portions that sufficiently match reference image  76 . 
         [0048]      FIG. 7  is a reproduction of an example masking image  78  detected from the example captured image  70  of  FIG. 5 . In this example, masking image  78  was generated by “thresholding” each pixel of captured image  70 . For example, each pixel of captured image  70  was analyzed to determine whether the intensity of the pixel was outside a pre-defined range, i.e., whether the pixel was very light or very dark. Based on the analysis, the corresponding pixel of the masking image  78  was set to a value of 0. All other pixels of the masking image  78  corresponding to pixels within captured image  70  that fell within the pre-defined range were set to a value of 255. In this way, masking image  78  was generated to mask out portions of the captured image  70  that may dominate or obscure the underlying background pattern  72 . The method of building the masking image may vary according to a type or characteristics of the captured image. 
         [0049]      FIGS. 8A-8C  show actual results obtained when different template matching techniques were performed to analyze the example captured image  70  of  FIG. 5 , and the results are shown for purposes of comparison.  FIG. 8A  shows an example matching result  82  obtained using direct fast Fourier transform (FFT) for a cross-correlation calculation, without normalization and without masking out retroreflective virtual image  74 , i.e., using the numerator of equation (1). As shown in  FIG. 8A , matching result  82  shows heavy interference by the additional security feature of the retroreflective virtual image as well as lighting unevenness. 
         [0050]      FIG. 8B  shows actual matching result  84  obtained using normalized cross-correlation (NCC) without masking out retroreflective virtual image  74 , i.e., using equation (1) above. As shown, lighting unevenness was improved, but the results are overnormalized due to the strong signal from retroreflective virtual image  74 . 
         [0051]      FIG. 8C  shows actual matching result  86  obtained using the fast masked NCC (FMNCC) techniques described herein. As can be seen from matching results  86  compared to matching results  84  and  82 , the FMNCC technique provides much more distinguished and consistent matching evaluation for all eight occurrences of the reference image  76  within the captured image  70 , as shown in the white peaks of the matching points. 
         [0052]      FIG. 9  illustrates an exemplary display image  90  presented by host system  20 . For example, host system  20  may output display image  90  on display  40 . In this example, host system  20  presents display image  90  to include captured image  70  of  FIG. 5 , and further includes identifiers marking matching regions  92  found to sufficiently match reference image  76 . As shown, the matching results consist of only the matching regions for which host system  20  found the entire reference image within the captured image. Each of the matching regions  92  is marked with a square, with a number indicating the rank of the matching region displayed at the center of each square. The rank of matching regions  92  that were rotated 180° from the reference image  76  are shown in brackets. 
         [0053]      FIG. 10  is an exemplary output window  94  presented by host system  20  on display  40  illustrating example template matching data. Output window  94  may be shown on display  40  ( FIG. 2 ) after host system  20  has analyzed captured image  70  obtained from the security document  12 , or this result may be combined with other parts of security confirmation of document authenticity. As illustrated in  FIG. 10 , output window  94  includes a row for each of six template matches found within captured image  70 . A location coordinate of the center pixel for each matching region  92  is displayed in column  96  in [x, y] form with the upper left corner as the origin. The matching regions  92  are ranked according to their correlation score, shown in column  98 , which is a number between 0 and 100, 100 being the highest. Rotation column  100  indicates the rotation in degrees of each of the matching regions  92  with respect to reference image  76 . 
         [0054]    Various embodiments of the invention have been described. Although described for purposes of explanation with respect to template matching of retroreflective images within security documents, the FMNCC techniques described herein may be applied to other forms of security features and template matching situations. These and other embodiments are within the scope of the following claims.