Abstract:
An automatic gain amplifier is disclosed that dynamically improves the quality of scanned biometric information. According to one embodiment, an image distribution of the scanned biometric is generated. Next, areas of higher image distribution are identified. An iterative process of adjusting the gain of, for instance, a capacitive sensing, is employed until an optimum separation of the areas of higher image distribution is achieved. Once the optimum separation is achieved, the gain is applied to the biometric sensing device so that biometric information can be scanned with improved image clarity. Electronic circuitry and software for implementing the methods are disclosed.

Description:
BACKGROUND 
     1. Field of the Invention 
     The present invention is related to an automatic gain amplifier, and more particularly to an automatic gain amplifier used in a biometric sensing device. 
     2. Background Information 
     In biometric imaging devices, for example optical-based or electrical properties-based fingerprint scanning devices, a user places a finger on a fingerprint sensor. The fingerprint sensor scans the fingerprint and generates an analog or digital signal that is representative of the scanned fingerprint. According to at least one imaging technique, a grayscale image is produced of the scanned fingerprint. Each pixel of the grayscale image has a value between 0 and 255—a value of 0 value representing a “black” pixel and a value of 255 representing a white pixel. Based on this grayscale image, biometric information is extracted. 
     FIG. 1 shows a diagram of a known fingerprint imaging device  2 . In normal operation, the fingerprint imaging device uses techniques derived from Coulombs law to determine the location of ridges and valleys in a fingerprint surface. By modeling each sensing element on the sensor as one plate in a capacitor and the finger surface (that is, the ridges and valleys) as the second plate in the capacitor, it is possible to measure a relative distance between the ridges and valleys to construct the fingerprint. 
     As is shown in FIG. 2, the fingerprint imaging device  2  is typically embodied in biometric sensor chip  4 , which consists of an m by n array of sensing elements or capacitive plates  6 . 
     A drawback to known biometric sensing devices, and in particular the imaging techniques employed, is that from individual to individual, and indeed, from situation to situation, the characteristics of the biometric, that is, the finger, can vary greatly. For instance, the moisture content of the finger may vary, as can the relative distance between ridges and valleys. Because these parameters vary, the resultant biometric image may not have the requisite image clarity needed when the biometric sensing device is deployed in a highly sensitive environment. 
     SUMMARY OF THE INVENTION 
     An automatic gain amplifier is disclosed that dynamically improves the quality of scanned biometric information. According to one embodiment, an image distribution of the scanned biometric is generated. Next, areas of higher image distribution are identified. An iterative process of adjusting the gain of a biometric sensor device, for instance, a capacitive sensing element, is employed until an optimum separation of the areas of higher image distribution is achieved. Once the optimum separation is achieved, the biometric sensing device analyzes the resulting image so that biometric information can be detected with improved image clarity and less interference with noise, such as dust and changing biometric parameters. Electronic circuitry and software for implementing the methods and apparatuses of the invention are disclosed. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 depicts the operational theory behind a known biometric sensing device. 
     FIG. 2 depicts a known fingerprint sensor. 
     FIG. 3 is a histogram showing the distribution of certain pixel-values sensed by a biometric sensing device. 
     FIG. 4 shows a conceptual diagram of the distribution of certain pixel values as inverted according to an embodiment of the invention. 
     FIG. 5 shows a conceptual diagram of the distribution of certain pixel values after having adjusted a gain of the biometric sensing device according to an embodiment of the invention. 
     FIG. 6 shows the desired distribution of certain pixel-values after having adjusted the gain of the biometric sensing device according to an embodiment of the invention. 
     FIG. 7 is an electrical schematic of the automatic gain amplifier according to an embodiment of the invention. 
     FIG. 8 is a block diagram of a microprocessor that can programmatically modify the gain of the automatic gain amplifier according to an embodiment of the invention. 
     FIG. 9 is a flowchart depicting the steps performed according to an embodiment of the invention. 
    
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
     FIG. 3 is a histogram  10  showing the pixel values collected by a biometric sensor device. The figure will be used to explain how the automatic gain amplifier of the present invention operates. The histogram  10  plots the frequency (or “counts”) of certain grayscale pixel values (or “pixel data”) as sensed by a biometric sensor device. The x-axis has an 8-bit range of grayscale values: 0 representing black, 255 representing white, and shades of gray in between. The y-axis corresponds to the frequency of occurrence of the corresponding x-axis values. 
     The distribution curve  12  can be said to show three regions. The first region  14  represents noise, as does the third region  18 . However, the second or “target” region  16  represents fingerprint minutiae information, such as the ridges and valleys of a fingerprint. Because fingerprints are often dry, there is a tendency for the ridges and valleys of the fingerprint to cluster near the white side of the histogram  10  when it is scanned by the biometric sensor device. The clustering tendency is illustrated by line  24 , which slices through the peak  20  of the true fingerprint pixel data, and the line  26 , which slices through the peak  22  of the white noise pixel data. Peaks  20  and  22  are initially separated by a distance (d)  25 . The invention seeks to counteract this clustering tendency and thereby improve the clarity and resolution of the sensed biometric image. 
     To this end, an automatic gain amplifier is disclosed that separates the first peak  20  and the second peak  22  by iteratively adjusting the gain of an amplifier that adjusts the sensitivity of the biometric sensor device. FIGS. 4-6 are diagrammatic representations of the gain adjustment process that is described in further detail with reference to FIG. 8 (below) 
     Turning to FIG. 4, the histogram  10  is first inverted to histogram  30 . Inverting the histogram  10  places the second peak  22  before the first peak  20  and the noise region  14  at the trailing end of the histogram  30 . 
     Next, as is shown FIG. 5, the gain of the operational amplifier is adjusted to yield histogram  32 . Adjusting the gain of the operational amplifier causes peaks  22  and  20  to separate by a distance  25 ′. Because the histogram  10  was inverted, the pixel values for the target region  16  are greater than the pixel values for the noise region  18 . Thus, amplifying the pixel values will have the affect of spreading the lower value regions less than the upper value regions. Thus, the resolution of the target region  16  is increased more than the corresponding resolution of the noise region  18 . Moreover, the noise region  14  will amplify out of range of the grayscale value—effectively filtering it from consideration. 
     Now, FIGS. 4 and 5 were conceptual diagrams. Histograms  30  and  32  were actually not generated by the electronics circuitry per se, but rather through software manipulation of the fingerprint pixel data sensed by the biometric sensor device. The histograms  30  and  32  are used for the purpose of explaining the automatic adjustment process, which, according to one embodiment, is performed by a microprocessor and corresponding control software. FIG. 6, however, shows the modified histogram  34  (histogram  32  inverted) resultant from the automatic gain amplification processes described above (and in further detail below). Notice the separation of the peaks  20  and  22 . Moreover, notice that the distribution area of the target region  16  is elongated—meaning that a higher resolution and improved biometric minutiae information clarity is achieved. 
     FIG. 7 shows electronic circuitry that comprises a significant portion of an embodiment of the automatic gain amplifier  36  according to one embodiment of the invention. Bias voltage (V bias )  40 , sample voltage (V a )  42 , and sample voltage (V b )  44  are fed into the operational amplifier  38 . The voltage inputs are not always coupled to the operational amplifier  38 . Instead a series of intervening switches  52  and  54 , under control of opposite edges of a clock cycle (not shown), dictate whether the voltage inputs are coupled and also determine whether any intervening capacitors are charging or discharging. According to one embodiment, the sample voltages  42  and  44  represent the charge at a sensing element in a capacitive sensor chip at two separate time intervals. 
     A first capacitor (C 1 )  46  is connected in series with bias voltage  40  and the negative input  39  of the operational amplifier  38 . A second capacitor (C 2 ) is connected between the sample voltage  42  and sample voltage  44 , and through switches  54  to the positive input  41  and negative input  39 . A third, variable capacitor (C 3 )  50 , under control of a microprocessor executing software code or hard-wired logic circuitry, is connected between the negative input  39  and the output  56  of the operational amplifier  38 . A second feedback line  51 , under control of switch  52 , is also connected between the negative input  39  and the output  56 . 
     The voltage output  56  of the automatic gain amplifier  36  is represented by EQ. 1.                V   out     =           C   1     ·     V   bias       +       C   2          (       V   b     -     V   a       )           C   3               (   1   )                                
     Selecting a value C for the first capacitor  46  and second capacitor  48  yields a gain for the automatic gain amplifier  36  that is inversely proportional to the variable capacitor  50  (that is, G m =C/C 3 ). 
     According to one embodiment, an 8-bit analog-to-digital converter is coupled to the voltage output  56 . The 8-bit analog-to-digital converter converts the analog voltage output  56  into a value that can be used by a microprocessor as an input to a software control algorithm. Alternatively, the conversion can take place at the microprocessor. 
     FIG. 8 depicts a microcontroller  58  that controls the variable capacitor  50 . The microcontroller includes a microprocessor  60 , a persistent memory  62 , and a volatile memory  64 , each connected through a common bus  61  to the microprocessor  60 . The microcontroller  58  further includes an input/output interface  66 . Fingerprint pixel data  56  is input through input line  68 , for example from voltage output  56 , into the microprocessor  60 . The microprocessor  60  stores the values in volatile memory  64  (or in an internal microprocessor memory) and executes software code, stored in persistent memory  62 , to manipulate and analyze the fingerprint pixel data  56 . The result is an output signal  70  that is used to control the variable capacitor  50 . The output signal  70  can be an analog or digital signal, so an intervening digital-to-analog converter may be employed. 
     FIG. 9 is a flowchart depicting the steps performed by the microcontroller  58  as it manipulates and analyzes the pixel data input to control the variable capacitor  50 . According to one embodiment, many of the steps are implemented in executable object code or another computer readable medium that is used to cause a microprocessor to perform the sequences of steps. For example, if the microcontroller  58  is utilized, the executable software code can be stored in persistent memory  62  and run from an execution memory area of the microprocessor  60 . 
     In step  72 , the biometric sensor device scans biometric information from, for example, a finger. The biometric image sensor device can be a capacitive fingerprint sensor or an optical fingerprint sensor. A presently preferred biometric image sensing device that can generate the pixel-values is the capacitive fingerprint sensing device disclosed in U.S. Pat. Nos. 6,016,355 and 6,049,620, which are incorporated herein by reference in their entirety. The capacitive fingerprint sensing device can also include a finger sensing element that enables the biometric sensor device, such as the device described in co-pending U.S. patent application Ser. No. 09/561,174, filed Apr. 27, 2002, entitled “METHOD AND APPARATUS FOR DETECTING THE PRESENCE OF A FINGER ON A BIOMETRIC SENSING DEVICE”, which is also incorporated herein by reference in its entirety. 
     In step  74 , the biometric information is transformed into pixel-based image data, for example a grayscale value. In step  76 , the frequency of the pixel values are counted, thus creating the histogram described above with references to FIG.  3 . In step  78 , two pixel values are selected as occurrence peaks, meaning they represent the peaks  20  and  22  described above. The selection of the occurrence peaks  20  and  22  effectively divides the histogram into the three regions. In step  80 , the separation between the peaks is determined, and in step  82  the separation value is compared against a threshold separation value (or values). 
     According to experiments by the inventors, acceptable separation values, when using 8-bit pixel values, are between  30  and  128  pixel values. In practice, a separation value of approximately  128  is ideal when 8-bit grayscale values store the pixel data. 
     If the separation value is below the threshold separation value, then the capacitance of the variable capacitor  50  is adjusted in step  84 . For instance, if the target region  16  and the noise region  18  are still too close, then the gain is increased, but if they are too far, then the gain is decreased. 
     According to one embodiment, an iterative successive approximations technique is used in step  84  whereby the capacitance adjustments are either halved or doubled depending on how the pixel data responds to the amplification. Assume, for example, that variable capacitance  50  corresponds to an 8-bit value. If the separation distance is too close, then the gain is increased by 128 units. If, upon the next pass through steps  72 - 82 , the separation distance is too far, then the gain is decreased by 64 units. And, after the next pass through steps  72 - 82 , if the separation distance is too close, then the gain is increased by 32 units, and so on until the ideal separation is achieved. According to other embodiments, a linear, a non-linear, a lookup-table based, or a fuzzy logic type control methodology can be implemented to adjust the output controlling the variable capacitance  50 . 
     After step  82 , if the separation value was within the threshold value (or values), then in step  86  the pixel values can then be further analyzed for specific biometric information, for instance fingerprint ridge end points or bifurcations. The specific biometric information can be used to enroll the particular biometric into a verification system, or it can be matched against enrolled biometric information stored in templates. Techniques for analyzing or matching the biometric information are disclosed in co-pending U.S. patent application Ser. Nos. 09/354,929, filed Jul. 15, 1999, and Ser. No. 09/501,355, filed Feb. 9, 2000, which are both incorporated herein by reference in their entirety. 
     Minor modifications to the invention are envisioned, but are not necessary for the proper operation of the automatic gain amplifier described herein. For instance, anti-spoofing technology may be employed either before or after adjusting the gain, such as the anti-spoofing technology described in U.S. Provisional Application Serial No. 60/158,458, filed Oct. 7, 1999, which is incorporated herein by reference in its entirety. Moreover, the histogram  10  may be filtered at either end of the pixel values before minutiae are extracted. Similarly, the collection of pixel values for selecting a sample set for the histogram  10 , or indeed even adjusting the gain itself, may be performed on a row by row, column by column, or even sub-region basis on the biometric sensor device. Accordingly, the written description and drawings are to be interpreted in an illustrative rather than a restrictive sense and are to be limited only by the accompanying claims.