CN108734074B - Fingerprint identification method and fingerprint identification device - Google Patents

Fingerprint identification method and fingerprint identification device Download PDF

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CN108734074B
CN108734074B CN201710705439.8A CN201710705439A CN108734074B CN 108734074 B CN108734074 B CN 108734074B CN 201710705439 A CN201710705439 A CN 201710705439A CN 108734074 B CN108734074 B CN 108734074B
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pixel values
standard deviation
object image
pixel
processor
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CN108734074A (en
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陈琮善
洪浚郎
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Gingy Technology Inc
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    • H—ELECTRICITY
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    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
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    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
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    • G—PHYSICS
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    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
    • G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V40/12—Fingerprints or palmprints
    • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
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    • H—ELECTRICITY
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    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
    • H01L27/146—Imager structures
    • H01L27/14601—Structural or functional details thereof
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
    • H01L27/146—Imager structures
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    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
    • H01L27/146—Imager structures
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    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
    • H01L27/146—Imager structures
    • H01L27/14683—Processes or apparatus peculiar to the manufacture or treatment of these devices or parts thereof
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    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L27/00—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate
    • H01L27/14—Devices consisting of a plurality of semiconductor or other solid-state components formed in or on a common substrate including semiconductor components sensitive to infrared radiation, light, electromagnetic radiation of shorter wavelength or corpuscular radiation and specially adapted either for the conversion of the energy of such radiation into electrical energy or for the control of electrical energy by such radiation
    • H01L27/144—Devices controlled by radiation
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    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1341—Sensing with light passing through the finger
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/14—Vascular patterns

Abstract

The invention provides a fingerprint identification method and a fingerprint identification device. The fingerprint identification method comprises the following steps: obtaining an object image, and storing a plurality of pixel data of the object image in a first color model format, wherein the plurality of pixel data comprises a plurality of first pixel values; converting the pixel data into a second color model format, and obtaining a plurality of second pixel values based on the converted pixel data and the first gain value; calculating a plurality of third pixel values according to the plurality of first pixel values and the plurality of second pixel values; calculating a first standard deviation according to the plurality of third pixel values; and judging whether the first standard deviation is higher than a first preset critical value, and if the first standard deviation is higher than the first preset critical value, identifying the object image as a fingerprint image of a real finger. The invention can effectively identify whether the object image is the fingerprint image of the real finger or not so as to effectively avoid the identification of fake finger.

Description

Fingerprint identification method and fingerprint identification device
Technical Field
The present invention relates to identification technologies, and in particular, to a fingerprint identification method and a fingerprint identification apparatus.
Background
The biometric identification category includes face, voice, iris, retina, vein, fingerprint identification, and the like. Since the fingerprint of each person is unique and the fingerprint is not easy to change with age or physical health condition, the fingerprint identification device has become one of the most popular biometric identification systems at present. According to different sensing methods, fingerprint identification devices can be classified into optical type, capacitive type, ultrasonic type, and thermal type.
However, because the conventional fingerprint recognition device cannot effectively recognize the difference between the real finger and the fake finger, a person who is not familiar with the fake finger made of the silica gel material usually has the fake finger made of the silica gel material, and the fake finger made of the silica gel material is simulated to have the fingerprint and the sweat pore. Therefore, after the fake finger with the silica gel characteristic and the fingerprint and sweat pore is pressed on the fingerprint identification device, the fake finger can also have the pressed finger deformation characteristic and the fingerprint and sweat pore characteristic to cheat the fingerprint identification device, and the fingerprint identification device can not correctly identify whether the fake finger is pressed by a real finger, so that a loophole in identification is caused. In view of this, the present invention will now propose several embodiments of solutions.
Disclosure of Invention
The invention provides a fingerprint identification device and a fingerprint identification method, which can provide a good fingerprint identification function and can effectively identify whether an object image is a fingerprint image of a real finger so as to effectively avoid fake finger identification.
The fingerprint identification method is suitable for the fingerprint identification device. The fingerprint identification method comprises the following steps: obtaining an object image, and storing a plurality of pixel data of the object image in a first color model format, wherein the plurality of pixel data comprises a plurality of first pixel values; converting the pixel data into a second color model format, and obtaining a plurality of second pixel values based on the converted pixel data and the first gain value; calculating a plurality of third pixel values according to the plurality of first pixel values and the plurality of second pixel values; calculating a first standard deviation according to the plurality of third pixel values; and judging whether the first standard deviation is higher than a first preset critical value, and if the first standard deviation is higher than the first preset critical value, identifying the object image as a fingerprint image of a real finger.
In an embodiment of the invention, the first color model format is a YUV color model format.
In an embodiment of the invention, the first pixel value data are a plurality of luminance values.
In an embodiment of the invention, the second color model format is an RGB color model format.
In an embodiment of the invention, the plurality of second pixel values and the plurality of third pixel values are a plurality of red pixel values. The first standard deviation is a red pixel value standard deviation.
In an embodiment of the invention, the step of obtaining the object image and storing the pixel data of the object image in the first color model format includes: a full object image is acquired, and a partial block of the full object image is sampled as the object image.
In an embodiment of the invention, the step of calculating the third pixel values according to the first pixel values and the second pixel values includes: subtracting the first pixel values from the second pixel values to obtain third pixel values.
In an embodiment of the present invention, the fingerprint identification method further includes the following steps: obtaining a plurality of fourth pixel values based on the plurality of pixel data after conversion and the second gain value; calculating a plurality of fifth pixel values according to the plurality of first pixel values and the plurality of fourth pixel values; and calculating a second standard deviation according to the plurality of fifth pixel values.
In an embodiment of the invention, the step of determining whether the first standard deviation is higher than the first preset threshold, and if the first standard deviation is higher than the first preset threshold, identifying the object image as the fingerprint image of the real finger includes: and further judging whether the second standard deviation is lower than a second preset critical value, and if the second standard deviation is lower than the second preset critical value, identifying the object image as the fingerprint image of the real finger.
In an embodiment of the invention, the fourth pixel values and the fifth pixel values are green pixel values, and the second standard deviation is a green pixel value standard deviation.
The fingerprint identification device comprises a storage device, a fingerprint sensor and a processor. The fingerprint sensor is used for acquiring an object image. The processor is coupled to the fingerprint sensor and the storage device. The processor is used for receiving the object image and storing a plurality of pixel data of the object image to the storage device in a first color model format. The processor converts the plurality of pixel data into a second color model format, and the processor obtains a plurality of second pixel values based on the converted plurality of pixel data and the first gain value. The processor calculates a plurality of third pixel values from the plurality of first pixel values and the plurality of second pixel values, and the processor calculates a first standard deviation from the plurality of third pixel values. The processor judges whether the first standard deviation is higher than a first preset critical value, and if the first standard deviation is higher than the first preset critical value, the processor identifies the object image as a fingerprint image of a real finger.
In an embodiment of the invention, the first color model format is a YUV color model format.
In an embodiment of the invention, the first pixel value data are a plurality of luminance values.
In an embodiment of the invention, the second color model format is an RGB color model format.
In an embodiment of the invention, the second pixel values and the third pixel values are red pixel values, and the first standard deviation is a standard deviation of the red pixel values.
In an embodiment of the invention, the processor obtains a complete object image, and the processor samples a partial area of the complete object image to serve as the object image.
In an embodiment of the invention, the processor subtracts the first pixel values from the second pixel values to obtain the third pixel values.
In an embodiment of the invention, the processor obtains a plurality of fourth pixel values based on the converted pixel data and the second gain value, and the processor calculates a plurality of fifth pixel values according to the plurality of first pixel values and the plurality of fourth pixel values. The processor calculates a second standard deviation from the fifth pixel values.
In an embodiment of the invention, the processor further determines whether the second standard deviation is lower than a second predetermined threshold. If the second standard deviation is lower than the second preset critical value, the processor identifies the object image as the fingerprint image of the real finger.
In an embodiment of the invention, the fourth pixel values and the fifth pixel values are green pixel values, and the second standard deviation is a green pixel value standard deviation.
Based on the above, the fingerprint identification apparatus and the fingerprint identification method of the present invention can obtain the standard deviation of the calculated specific pixel values of at least a portion of the object image by analyzing and calculating the object image. In addition, the fingerprint identification device can judge the size of the standard deviation through a preset critical value to effectively identify whether the object image belongs to the fingerprint image of the real finger or not so as to avoid the fake finger from passing the identification.
In order to make the aforementioned and other features and advantages of the invention more comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
FIG. 1 is a block diagram of a fingerprint recognition device according to an embodiment of the present invention.
FIG. 2 is a schematic diagram of an object image according to an embodiment of the invention.
FIG. 3 is a diagram illustrating an embodiment of converting a color model format of pixel data of an object image.
Fig. 4A is a schematic diagram illustrating an object image being adjusted based on a first gain value according to an embodiment of the invention.
FIG. 4B is a diagram illustrating an embodiment of adjusting an object image based on a second gain value.
FIG. 5 is a diagram illustrating an embodiment of computing pixel data of an object image.
FIG. 6 is a flow chart of a fingerprint identification method according to an embodiment of the present invention.
Description of the reference numerals
100: a fingerprint recognition device;
110: a processor;
120: a fingerprint sensor;
130: a storage device;
200: an object image;
210: a fingerprint;
220. 410, 420: a partial fingerprint image;
310. 320, 510: a data matrix;
s610, S620, S630, S640, S650: and (5) carrying out the following steps.
Detailed Description
In order that the contents of the present invention may be more clearly understood, a plurality of embodiments are set forth below to illustrate the present invention, however, the present invention is not limited to the illustrated plurality of embodiments. Suitable combinations between the embodiments are also allowed. Further, wherever possible, the same reference numbers will be used throughout the drawings and the description to refer to the same or like parts.
FIG. 1 is a block diagram of a fingerprint recognition device according to an embodiment of the present invention. Referring to fig. 1, in the present embodiment, a fingerprint recognition device 100 includes a processor 110, a fingerprint sensor 120, and a storage device 130. The processor 110 is coupled to the fingerprint sensor 120 and the storage device 130. In the present embodiment, the fingerprint sensor 120 is used for acquiring an object image and providing the object image to the processor 110, so that the processor 110 analyzes the object image. Storage device 130 stores a number of program modules. The processor 110 may read these program modules of the storage device 130 to implement the fingerprint identification method according to the embodiments of the present invention. Furthermore, the method is simple. In the present embodiment, the fingerprint sensor 120 may include a light source, a light receiver, and the like, which are optical fingerprint sensing elements, but the present invention is not limited thereto. In an embodiment, the fingerprint sensor 120 may also include a capacitive or other type of fingerprint sensing element.
In the embodiment, the Processor 110 is, for example, a Central Processing Unit (CPU), a System On Chip (SOC), or other Programmable general purpose or special purpose microprocessor (microprocessor), a Digital Signal Processor (DSP), a Programmable controller, an Application Specific Integrated Circuit (ASIC), a Programmable Logic Device (PLD), other similar Processing devices, or a combination thereof.
In the present embodiment, the storage device 130 is, for example, any type of fixed or removable Random Access Memory (RAM), Read-Only Memory (ROM), flash Memory (flash Memory), or the like or combination thereof. In the embodiment, the storage device 130 is used to store the object image data and the program modules of the embodiments of the invention, so that the processor 110 can read the storage device 130 and execute the data and the program modules to implement the fingerprint identification method of the embodiments of the invention.
FIG. 2 is a schematic diagram of an object image according to an embodiment of the invention. Refer to fig. 1 and 2. In the present embodiment, the object image 200 includes a plurality of pixel data, and the object image 200 may be a complete object image. In the present embodiment, the fingerprint sensor 120 provides the object image 200 to the processor 110, and the processor 110 stores a plurality of pixel data of the object image 200 in a first color model. In the embodiment, the first color model is in YUV color model format, but the invention is not limited thereto. In one embodiment, the first color model may be in other types of color model formats.
In the present embodiment, the object image 200 acquired by the fingerprint sensor 120 may be a complete object image, and the object image 200 may include the fingerprint 210, for example. However, in the present embodiment, the processor 110 may acquire a portion of the object image 200 for analysis. That is, the processor 110 may sample a portion of the object image 220 of the object image 200, and perform the following image analysis and recognition operations on the portion of the object image 220. For example, the object image 200 may have a pixel number of 320 × 240, for example, and the partial object image 220 may have a pixel number of 60 × 60, for example. The processor 110 may obtain the object image 200 at the center or at the position of the important feature, and the invention is not limited thereto. Therefore, the processor 110 of the present embodiment can reduce the computation of image analysis, and can effectively determine whether the object image belongs to the fingerprint image of the real finger.
FIG. 3 is a diagram illustrating an embodiment of converting a color model format of pixel data of an object image. Referring to fig. 1 to 3, in the present embodiment, the processor 110 may analyze the partial object image 220 to obtain pixel data Y (0,0)/U (0,0)/V (0,0) to Y (3,3)/U (3,3)/V (3,3) of each pixel in the partial object image 220. Specifically, such as data matrix 310 of fig. 3. The partial object image 220 has, for example, a number of pixels of 4 × 4. In the present embodiment, the pixel data Y (0,0)/U (0,0)/V (0,0) -Y (3,3)/U (3,3)/V (3,3) of each pixel in the partial object image 220 is data in the YUV color model format. The processor 110 may convert the pixel data Y (0,0)/U (0,0)/V (0,0) to Y (3,3)/U (3,3)/V (3,3) into a second color model format according to the following formulas (1) to (3), where the second color model format is, for example, an RGB color model format, but the invention is not limited thereto. In one embodiment, the second color model may also be in other types of color model formats. The following equations (1) to (3):
.
The equation (2) is used to solve the problem of high efficiency of the method, namely, Y-0.39465 (U-128) -0.58060 (V-128)
.
Therefore, as in the data matrix 320 of fig. 3, the processor 110 converts the pixel data Y (0,0)/U (0,0)/V (0,0) -Y (3,3)/U (3,3)/V (3,3) into a plurality of pixel data R (0,0)/G (0,0)/B (0,0) -R (3,3)/G (3,3)/B (3,3) in the RGB color model format. In an embodiment, the processor 110 may further adjust the pixel data R (0,0)/G (0,0)/B (0,0) to R (3,3)/G (3,3)/B (3,3) by different gain values, so as to perform the analysis operation by using the pixel data after the gain value adjustment.
Fig. 4A is a schematic diagram illustrating an object image being adjusted based on a first gain value according to an embodiment of the invention. FIG. 4B is a diagram illustrating an embodiment of adjusting an object image based on a second gain value. Referring to fig. 1 to 4B, for example, the processor 110 may adjust the pixel data R (0,0)/G (0,0)/B (0,0) -R (3,3)/G (3,3)/B (3,3) of the data matrix 320 of fig. 3 by a first gain value and a second gain value, respectively. In the embodiment, the first Gain value may be, for example, an RGB Gain (Gain) of 1:1:1, and the second Gain value may be, for example, an RGB Gain of 1:2:2, but the invention is not limited thereto. Therefore, the adjusted partial object image 410 may be a lighter image, and the adjusted partial object image 420 may be a darker image. However, the ratio of the gain values is not limited thereto, and in an embodiment, the ratio of the first gain value and the second gain value may be determined according to different usage requirements or requirements of the fingerprint identification device.
FIG. 5 is a diagram illustrating an embodiment of computing pixel data of an object image. Referring to fig. 1 to 5, in the present embodiment, the processor 110 may adjust the pixel data R (0,0)/G (0,0)/B (0,0) to R (3,3)/G (3,3)/B (3,3) through the first gain value, and then calculate the pixel data of each pixel according to the following equations (4) to (6). The equations (4) to (6) are as follows:
Δ YR (i, j) ═ Y (i, j) -R (i, j.... equation (4)
Δ YG (i, j) ═ Y (i, j) -G (i, j) ·
Equation (6) is given as Δ YB (i, j) ═ Y (i, j) -B (i, j)
In the above formulas (4) to (6), i and j are positive integers greater than 0. Therefore, as shown in the data matrix 510 of fig. 5, the processor 110 may take a plurality of pixel data Δ YR (0,0)/Δ YG (0,0)/Δ YB (0,0) - Δ YR (3,3)/Δ YG (3,3)/Δ YB (3, 3).
It should be noted that, in the present embodiment, the processor 110 calculates the standard deviation according to at least one type of pixel values of the pixel data Δ YR (0,0)/Δ YG (0,0)/Δ YB (0,0) - Δ YR (3,3)/Δ YG (3,3)/Δ YB (3, 3). For example, first, the processor 110 may use the luminance values Y (0,0) -Y (3,3) of each pixel in the data matrix 310 of fig. 3 as a plurality of first pixel values, and use the red pixel values R (0,0) -R (3,3) of each pixel in the data matrix 320 of fig. 3 as a plurality of second pixel values after being adjusted by the first gain value. Next, the processor 110 subtracts the first pixel values from the second pixel values to obtain a plurality of pixel values Δ YR (0,0) to Δ YR (3,3) of each pixel in the data matrix 510 of fig. 5. The processor 110 takes the pixel values Δ YR (0,0) to Δ YR (3,3) as a plurality of third pixel values, and the processor 110 calculates a first standard deviation of the third pixel values according to the following equations (7) and (8). The formula (7) and the formula (8) are as follows:
Figure BDA0001381351860000071
Figure BDA0001381351860000081
among the above formulas (7) and (8), XkAnd Δ YR (0,0) to Δ YR (3, 3). Thus, the processor 110 may obtain the standard deviation sd (r) corresponding to the pixel values Δ YR (0,0) to Δ YR (3, 3). In the present embodiment, the processor 110 determines whether the standard deviation sd (r) is higher than a first predetermined threshold. If the standard deviation sd (r) is higher than the first predetermined threshold, the processor 110 identifies the object image 200 as a fingerprint image of a real finger. That is, since the fingerprint image of the real finger has a specific flesh color, it can be effectively distinguished that the object image belongs to the fingerprint image of the real finger or the fake finger by calculating the standard deviation with respect to the specific pixel value.
In this embodiment, the adjusted and calculated standard deviation sd (r) of the red pixel value of the fingerprint image of the real finger should be higher than the first predetermined threshold. Otherwise, the red pixel value of the fingerprint image of the fake finger will not be higher than the first predetermined threshold value through the adjustment and the calculated standard deviation sd (r). Therefore, the fingerprint identification device 100 of the present embodiment can identify whether the object image belongs to the fingerprint image of the real finger according to the above determination method.
For another example, the standard deviation calculation method described above can also be applied to calculate the second standard deviation. First, the processor 110 may use the luminance values Y (0,0) -Y (3,3) of each pixel in the data matrix 310 of fig. 3 as a plurality of first pixel values, and use the green pixel values G (0,0) -G (3,3) of each pixel in the data matrix 320 of fig. 3 as a plurality of fourth pixel values after being adjusted by the second gain value. Next, the processor 110 subtracts the first pixel values from the fourth pixel values to obtain a plurality of pixel values Δ YG (0,0) - Δ YG (3,3) of each pixel in the data matrix 510 of fig. 5. The processor 110 takes the pixel values Δ YG (0,0) - Δ YG (3,3) as a plurality of fifth pixel values, and the processor 110 calculates the standard deviation sd (g) of the fifth pixel values according to the above formulas (7) and (8).
Therefore, the processor 110 can obtain the standard deviation sd (g) corresponding to the pixel values Δ YG (0,0) - Δ YG (3, 3). In this embodiment, the processor 110 determines whether the standard deviation sd (g) is higher than a second predetermined threshold. If the standard deviation sd (g) is higher than the second predetermined threshold, the processor 110 identifies the object image 200 as a fingerprint image of a real finger. That is, since the color of the fake finger may be flesh color, the fingerprint identification apparatus 100 of the present invention can further determine the standard deviation sd (g) calculated by the fifth pixel values after the adjustment of the second gain value, in addition to the standard deviation sd (r) calculated by the determination of the third pixel values after the adjustment of the first gain value, so as to effectively avoid passing the identification of the fake finger whose color is flesh color.
In this embodiment, the adjusted and calculated standard deviation sd (g) of the green pixel value of the fingerprint image of the real finger should be higher than the second predetermined threshold. Otherwise, the green pixel value of the fingerprint image of the fake finger will not be higher than the second predetermined threshold value through the above adjustment and the calculated standard deviation sd (g). Therefore, the fingerprint identification device 100 of the present embodiment can identify whether the object image belongs to the fingerprint image of the real finger according to the above determination method.
The experimental results of the samples of table 1 are presented further below to assist in the description of the above-described exemplary embodiments.
Figure BDA0001381351860000091
TABLE 1
The recognition results of a plurality of samples are exemplified. According to table 1, the samples include sample FAKE1, sample FAKE7, and sample TRUE. In the present embodiment, the processor 110 obtains the standard deviations calculated by the standard deviation calculation method described in the above embodiment, after the red, green and blue pixel values of each pixel and the corresponding brightness values of the partial object images of the samples are respectively adjusted by the first GAIN value (GAIN a) and the second GAIN value (GAIN B).
For example, in the present embodiment, the processor 110 can respectively determine whether the standard deviation sd (r) of the first GAIN values (GAIN a) of the samples is higher than a predetermined threshold of 30. And the processor 110 further determines whether the standard deviation sd (g) of the second GAIN values (GAIN B) of the samples is lower than a preset threshold of 30. In other words, the processor 110 may identify whether this image is obtained from a real finger through images of different degrees of color cast. Therefore, in table 1 above, since only the sample TRUE meets the two standard deviation conditions, the processor 110 can determine that the sample TRUE is the fingerprint image of the real finger. However, in an embodiment, the processor 110 may also set a plurality of predetermined threshold values to respectively determine the standard deviation of other pixel values adjusted by different gain values. Alternatively, the processor 110 may set one or more predetermined threshold values to determine the standard deviation of at least one pixel value adjusted by a single gain value, but the invention is not limited thereto.
It should be noted that after the object image 200 passes the above-mentioned identification operation, the fingerprint identification apparatus 100 may further perform a fingerprint authentication operation on the object image 200 to determine whether the fingerprint feature in the object image 200 matches the fingerprint feature registered by the fingerprint identification apparatus 100 in advance. However, the fingerprint authentication operations described in the embodiments of the present invention are sufficient for those skilled in the art to obtain sufficient teachings, suggestions and implementations according to the techniques in the field, and thus are not described herein in detail.
FIG. 6 is a flow chart of a fingerprint identification method according to an embodiment of the present invention. Referring to fig. 1 and 6, the fingerprint identification method of fig. 6 may be at least applied to the fingerprint identification device 100 of fig. 1. In step S610, the processor 110 obtains an object image through the fingerprint sensor 120, and stores a plurality of pixel data of the object image in a first color model format to the storage device 130, wherein the pixel data includes a plurality of first pixel values. In step S620, the processor 110 converts the pixel data into a second color model format, and obtains a plurality of second pixel values based on the converted pixel data and the first gain value. In step S630, the processor 110 calculates a plurality of third pixel values according to the first pixel values and the second pixel values. In step S640, the processor 110 calculates a first standard deviation according to the third pixel values. In step S650, the processor 110 determines whether the first standard deviation is higher than a first predetermined threshold, and identifies the object image as a fingerprint image of a real finger if the first standard deviation is higher than the first predetermined threshold. Therefore, the fingerprint identification method of the embodiment can effectively identify whether the object image is the fingerprint image of the real finger or not so as to effectively avoid the passing identification of the forged finger.
In addition, the related embodiments and the component features of the fingerprint identification device 100 can be obtained from the contents of the embodiments of fig. 1 to 5 to obtain sufficient teaching, suggestion and implementation descriptions, and thus are not repeated herein.
In summary, the fingerprint identification apparatus and the fingerprint identification method of the present invention can acquire at least a portion of the object image for analysis. First, the fingerprint identification device of the present invention can adjust a plurality of pixel values of the object image according to different gain values. Then, the fingerprint identification device of the invention can further calculate the pixel values of the part of the object image to obtain the standard deviation corresponding to the pixel values. Finally, the fingerprint identification device of the invention can judge the size of the standard deviation through a preset critical value so as to determine whether the object image belongs to the fingerprint image of the real finger. Therefore, the fingerprint identification device and the fingerprint identification method can effectively avoid fake finger passing identification.
Although the present invention has been described with reference to the above embodiments, it should be understood that various changes and modifications can be made therein by those skilled in the art without departing from the spirit and scope of the invention.

Claims (18)

1. A fingerprint identification method, comprising:
obtaining an object image, and storing a plurality of pixel data of the object image in a first color model format, wherein the plurality of pixel data comprises a plurality of first pixel values;
converting the pixel data into a second color model format, and obtaining a plurality of second pixel values based on the converted pixel data and the first gain value;
calculating a plurality of third pixel values according to the plurality of first pixel values and the plurality of second pixel values;
calculating a first standard deviation according to the plurality of third pixel values; and
judging whether the first standard deviation is higher than a first preset critical value, if so, identifying the object image as a fingerprint image of a real finger,
wherein the step of calculating the third pixel values according to the first pixel values and the second pixel values comprises:
subtracting the first pixel values from the second pixel values to obtain third pixel values.
2. The fingerprint recognition method of claim 1, wherein the first color model format is a YUV color model format.
3. The fingerprint recognition method of claim 1, wherein the plurality of first pixel values are a plurality of luminance values.
4. The fingerprint recognition method of claim 1, wherein the second color model format is an RGB color model format.
5. The fingerprint recognition method of claim 1, wherein the plurality of second pixel values and the plurality of third pixel values are a plurality of red pixel values, and the first standard deviation is a red pixel value standard deviation.
6. The fingerprint recognition method of claim 1, wherein the step of obtaining the object image and storing the plurality of pixel data of the object image in the first color model format comprises:
a full object image is acquired, and a partial block of the full object image is sampled as the object image.
7. The fingerprint recognition method of claim 1, further comprising:
obtaining a plurality of fourth pixel values based on the plurality of pixel data after conversion and the second gain value;
calculating a plurality of fifth pixel values according to the plurality of first pixel values and the plurality of fourth pixel values; and
and calculating a second standard deviation according to the plurality of fifth pixel values.
8. The fingerprint identification method according to claim 7, wherein the step of determining whether the first standard deviation is higher than the first predetermined threshold value, and if the first standard deviation is higher than the first predetermined threshold value, identifying the object image as the fingerprint image of the real finger comprises:
and further judging whether the second standard deviation is lower than a second preset critical value, and if the second standard deviation is lower than the second preset critical value, identifying the object image as the fingerprint image of the real finger.
9. The fingerprint recognition method of claim 7, wherein the plurality of fourth pixel values and the plurality of fifth pixel values are a plurality of green pixel values, and the second standard deviation is a green pixel value standard deviation.
10. A fingerprint recognition device, comprising:
a storage device;
the fingerprint sensor is used for acquiring an object image; and
a processor coupled to the fingerprint sensor and the storage device, the processor configured to receive the object image and store a plurality of pixel data of the object image in a first color model format to the storage device,
wherein the processor converts the plurality of pixel data into a second color model format and the processor derives a plurality of second pixel values based on the converted plurality of pixel data and a first gain value,
wherein the processor calculates a plurality of third pixel values from the plurality of first pixel values and the plurality of second pixel values, and the processor calculates a first standard deviation from the plurality of third pixel values,
wherein the processor determines whether the first standard deviation is higher than a first preset critical value, and if the first standard deviation is higher than the first preset critical value, the processor identifies the object image as a fingerprint image of a real finger,
the processor subtracts the first pixel values from the second pixel values to obtain third pixel values.
11. The fingerprint recognition device of claim 10, wherein the first color model format is a YUV color model format.
12. The apparatus of claim 10, wherein the first pixel values are luminance values.
13. The fingerprint recognition device of claim 10, wherein the second color model format is an RGB color model format.
14. The apparatus of claim 10, wherein the second and third pixel values are red pixel values, and the first standard deviation is a red pixel value standard deviation.
15. The fingerprint recognition device of claim 10, wherein the processor obtains a complete object image, and the processor samples a portion of the complete object image as the object image.
16. The apparatus of claim 10, wherein the processor obtains a plurality of fourth pixel values based on the converted pixel data and the second gain value, and the processor calculates a plurality of fifth pixel values according to the first pixel values and the fourth pixel values,
wherein the processor calculates a second standard deviation from the fifth plurality of pixel values.
17. The apparatus of claim 16, wherein the processor further determines whether the second standard deviation is lower than a second predetermined threshold, and if the second standard deviation is lower than the second predetermined threshold, the processor identifies the object image as the fingerprint image of the real finger.
18. The fingerprint recognition device of claim 16, wherein the plurality of fourth pixel values and the plurality of fifth pixel values are a plurality of green pixel values, and the second standard deviation is a green pixel value standard deviation.
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