CN112464866A - Fingerprint sensing device and fingerprint sensing method - Google Patents
Fingerprint sensing device and fingerprint sensing method Download PDFInfo
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- 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
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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
- G06V40/1306—Sensors therefor non-optical, e.g. ultrasonic or capacitive sensing
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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
- G06V40/1347—Preprocessing; Feature extraction
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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
- G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
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Abstract
The invention provides a fingerprint sensing device and a fingerprint sensing method. The fingerprint sensing device comprises a sensor and a comparison module. The sensor respectively acquires a first fingerprint image and a second fingerprint image at a first time point and a second time point. The comparison module compares the first fingerprint image with the second fingerprint image according to at least one judgment standard so as to judge whether the finger is a real finger.
Description
Technical Field
The present invention relates to a fingerprint sensing device and a fingerprint sensing method, and more particularly, to a fingerprint sensing device and a fingerprint sensing method capable of identifying whether a finger image is a real finger image.
Background
In recent years, biometric sensing technology is widely applied to various electronic devices or terminal devices to provide various identity login or identity authentication functions. In particular, fingerprint sensing and palm print sensing are currently common sensing schemes. Generally, a user can press a finger on a sensor to make the sensor obtain a fingerprint image for a subsequent identification or verification operation.
As information security becomes more and more important, security of fingerprint recognition becomes more and more important. However, it is possible for a malicious person to unlock the device by using a fingerprint remaining on the object in an attempt to pass the authentication. Therefore, how to judge whether the fingerprint comes from the surface printing is one of the important research directions in the field.
Disclosure of Invention
The invention provides a fingerprint sensing device and a fingerprint sensing method capable of identifying whether a finger image is a real finger image.
The fingerprint sensing device comprises a sensor and a comparison module. The sensor respectively acquires a first fingerprint image and a second fingerprint image at a first time point and a second time point. The comparison module is coupled to the sensor. The comparison module compares the first fingerprint image with the second fingerprint image according to at least one judgment standard so as to judge whether the finger is a real finger.
The fingerprint sensing method is suitable for the fingerprint sensing device. The fingerprint sensing device comprises a sensor and a comparison module. The fingerprint sensing method comprises the following steps: respectively acquiring a first fingerprint image and a second fingerprint image at a first time point and a second time point by a sensor; and comparing the first fingerprint image with the second fingerprint image by the comparison module according to at least one judgment standard so as to judge whether the finger is a real finger.
Based on the above, the fingerprint sensing device and the fingerprint sensing method of the invention can acquire the first fingerprint image and the second fingerprint image at different time points, and compare the first fingerprint image and the second fingerprint image according to at least one judgment standard, thereby judging whether the finger is a real finger. Therefore, the invention can improve the anti-counterfeiting effect of fingerprint sensing.
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 schematic diagram of a fingerprint sensing device according to an embodiment of the present invention;
FIG. 2 is a flowchart of a method of fingerprint sensing according to a first embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating a first fingerprint image and a second fingerprint image of a real finger according to an embodiment of the present invention;
FIG. 4 is a flowchart of a method of fingerprint sensing according to a second embodiment of the present invention;
FIG. 5 is a flowchart of a fingerprint sensing method according to a third embodiment of the present invention;
FIG. 6 is a diagram illustrating a second fingerprint image of a fake finger according to one embodiment of the present invention;
FIG. 7 is a diagram illustrating a second fingerprint image of a fake finger according to one embodiment of the present invention;
fig. 8 is a flowchart illustrating a fingerprint sensing method according to a fourth embodiment of the present invention.
Description of the reference numerals
100: a fingerprint sensing device;
110: a sensor;
120: a comparison module;
s100, S200, S300, S400: a fingerprint sensing method;
FG: a finger;
F1-F6: fingerprint features;
IM 1: a first fingerprint image;
IM 2: a second fingerprint image;
R1-R3: an area;
s110 and S120: a step of;
s210 to S250: a step of;
s310 to S370: a step of;
s410 to S450: and (5) carrying out the following steps.
Detailed Description
Reference will now be made in detail to exemplary embodiments of the invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings and the description to refer to the same or like parts.
Referring to fig. 1 and fig. 2, fig. 1 is a schematic diagram of a fingerprint sensing device according to a first embodiment of the invention. In the present embodiment, the fingerprint sensing device 100 includes a sensor 110 and a comparison module 120. The fingerprint sensing method S100 is applicable to the fingerprint sensing device 100. In step S110, the sensor 110 acquires a first fingerprint image IM1 and a second fingerprint image IM2 of the finger FG at a first time point and a second time point, respectively. In the present embodiment, the sensor 110 can be, for example, an optical fingerprint sensor, a capacitive fingerprint sensor or an ultrasonic fingerprint sensor, and the optical fingerprint sensor can be, for example, a Complementary Metal Oxide Semiconductor (CMOS) sensor, a Charge Coupled Device (CCD) or an image acquisition Device with similar functions.
Specifically, in step S110, the first time point is different from the second time point. The time interval between the second time point and the first time point is less than or equal to 0.5 seconds (e.g., 0.1 seconds, but the invention is not limited thereto). The sensor 110 will acquire a first fingerprint image IM1 at a first point in time. The sensor 110 acquires the second fingerprint image IM2 at a second time point after the first time point. For example, based on the operation of the fingerprint sensing device 100 by the finger FG, the sensor 110 acquires a first fingerprint image IM1 generated by the finger FG tapping (or just touching) the fingerprint sensing device 100 at a first point in time, and acquires a second fingerprint image IM2 generated by the finger FG pressing the fingerprint sensing device 100 at a second point in time. In step S120, the comparison module 120 is coupled to the sensor 110 to receive the first fingerprint image IM1 and the second fingerprint image IM 2. The comparison module 120 compares the first fingerprint image IM1 and the second fingerprint image IM2 according to at least one determination criterion to determine whether the finger FG is a real finger. That is, the comparison module 120 can identify whether the image of the finger FG is a real finger image according to the determination criterion, so as to determine whether the finger FG is a real finger from the real finger image. Thus, the anti-counterfeiting effect of fingerprint sensing can be improved. In this embodiment, the determination criteria include, for example, area of the fingerprint image region, number of fingerprint features, correlation of fingerprint patterns, and the like. In the embodiment, the comparing module 120 is, for example, a Central Processing Unit (CPU), or other Programmable general purpose or special purpose Microprocessor (Microprocessor), Digital Signal Processor (DSP), Programmable controller, Application Specific Integrated Circuit (ASIC), Programmable Logic Device (PLD), or other similar devices or combinations thereof, which can load and execute computer programs.
For example, referring to fig. 1 and fig. 3 together, fig. 3 is a schematic diagram illustrating a first fingerprint image and a second fingerprint image of a real finger according to an embodiment of the present invention. Based on the operation (e.g., tap-to-press) of the fingerprint sensing device 100 by the real finger FG, the area of the fingerprint image of the finger FG acquired by the fingerprint sensing device 100 and the number of fingerprint features may increase with time. In this embodiment, after receiving the first fingerprint image IM1 and the second fingerprint image IM2, the comparison module 120 defines a region R1 of the first fingerprint image IM1, and defines a region R2 similar to the region R1 and a region R3 outside the region R2 in the second fingerprint image IM 2. In the present embodiment, the first fingerprint image IM1 has a region R1. The second fingerprint image IM2 has regions R2, R3. The region R1 is an image range of the first fingerprint image IM1 or a fitting pattern of a plurality of fingerprint features surrounding the first fingerprint image IM 1. The regions R2, R3 are image ranges of the second fingerprint image IM2 or a plurality of fitted figures of a plurality of fingerprint features surrounding the second fingerprint image IM 2. The number and shape of the regions of the present invention are not limited to the embodiment. Fingerprint features may be ridge endpoints, bifurcations, or short ridges (or referred to as outliers) that are local to the fingerprint.
In the present embodiment, in the case where the fingerprint sensing device 100 is operated by the real finger FG, the region R1 is similar to the region R2. That is, the second fingerprint image IM2 increases the area of the region R3 compared to the first fingerprint image IM 1. Therefore, when the area of the region of the second fingerprint image IM2 (i.e., the total area of the regions R2, R3) is determined to be larger than the area of the region of the first fingerprint image IM1 (i.e., the area of the region R1), the comparison module 120 determines that the finger FG is a real finger. On the other hand, when the area of the second fingerprint image IM2 is determined to be smaller than or equal to the area of the first fingerprint image IM1, the comparison module 120 determines that the finger FG is a fake finger.
For another example, in the present embodiment, based on the operation of the fingerprint sensing device 100 by the real finger FG, the region R1 of the first fingerprint image IM1 has 3 fingerprint features F1, F2, F3. The region R2 of the second fingerprint image IM2 has 3 fingerprint features F1, F2, F3. The region R3 of the second fingerprint image IM2 has 2 fingerprint features F4, F5. Therefore, when the number of fingerprint features of the second fingerprint image IM2 (i.e., the total number of fingerprint features F1-F5 of the regions R2 and R3) is determined to be greater than the number of fingerprint features of the first fingerprint image IM1 (i.e., the total number of fingerprint features F1-F3 of the region R1), the comparison module 120 determines that the finger FG is a real finger. On the other hand, when the number of fingerprint features of the second fingerprint image IM2 is determined to be less than or equal to the number of fingerprint features of the first fingerprint image IM1, the comparison module 120 determines that the finger FG is a fake finger.
Referring to fig. 1, fig. 3 and fig. 4, fig. 4 is a flowchart illustrating a fingerprint sensing method according to a second embodiment of the invention. In the present embodiment, the fingerprint sensing method S200 is applied to the fingerprint sensing device 100. In step S210, the sensor 110 acquires a first fingerprint image IM1 and a second fingerprint image IM2 of the finger FG at a first time point and a second time point, respectively. In step S220, the comparison module 120 receives the first fingerprint image IM1 and the second fingerprint image IM2 of the finger FG, and determines whether the number of fingerprint features of the second fingerprint image IM2 is greater than that of the first fingerprint image IM 1. When the number of fingerprint features of the second fingerprint image IM2 is determined to be greater than that of the first fingerprint image IM1, the comparison module 120 determines that the finger FG is an authentic finger in step S230. In the case that the finger FG is determined to be a real finger, the comparison module 120 may further identify the identity of the user corresponding to the finger FG in step S240. In some embodiments, the fingerprint sensing device 100 may be provided in an electronic device, and the fingerprint sensing device 100 may inform the electronic device to identify the identity of the user corresponding to the finger FG in step S240.
On the other hand, when the number of fingerprint features of the second fingerprint image IM2 is determined to be less than or equal to the number of fingerprint features of the first fingerprint image IM1 in step S220, the comparison module 120 determines that the finger FG is a fake finger in step S250. If the finger FG is determined to be a fake finger, the identity of the user of the finger FG is not recognized.
In some embodiments, in addition to comparing the number of fingerprint features of the first fingerprint image IM1 with the number of fingerprint features of the second fingerprint image IM2, the comparison module 120 further compares the area of the fingerprint image area of the first fingerprint image IM1 with the area of the fingerprint image area of the second fingerprint image IM 2. Details regarding the comparison of the area of the fingerprint image area of the first fingerprint image IM1 with the area of the fingerprint image area of the second fingerprint image IM2 can be sufficiently taught by the embodiments of fig. 1 and fig. 3, and therefore cannot be reiterated here.
In some embodiments, the comparison module 120 further determines whether the contrast of the fingerprint features of the first fingerprint image IM1 and the contrast of the fingerprint features of the second fingerprint image IM2 are greater than a default value. The default value may be a contrast default value defined by a user or designer. The contrast is, for example, a grayscale difference between a fingerprint ridge image and a fingerprint valley image associated with the fingerprint image. When the contrast of at least one of the fingerprint features of the first fingerprint image IM1 and the fingerprint features of the second fingerprint image IM2 is less than or equal to a default value, the fingerprint features less than or equal to the default value are not clear. Therefore, the comparison module 120 determines that the finger FG is a fake finger.
Referring to fig. 1, fig. 3 and fig. 5, fig. 5 is a flowchart illustrating a fingerprint sensing method according to a third embodiment of the invention. In the present embodiment, the fingerprint sensing method S300 is applied to the fingerprint sensing device 100. In step S310, the sensor 110 acquires a first fingerprint image IM1 and a second fingerprint image IM2 of the finger FG at a first time point and a second time point, respectively. In step S320, the comparison module 120 determines whether the number of fingerprint features of the second fingerprint image IM2 is greater than the number of fingerprint features of the first fingerprint image IM 1. When the number of fingerprint features of the second fingerprint image IM2 is determined to be smaller than or equal to the number of fingerprint features of the first fingerprint image IM1, the comparison module 120 determines that the finger FG is a fake finger in step S330. On the other hand, when the number of fingerprint features of the second fingerprint image IM2 is determined to be greater than that of the first fingerprint image IM1, the comparing module 120 determines in step S340 whether the number of fingerprint features in the region R1 of the first fingerprint image IM1 is equal to that in the region R2 of the second fingerprint image IM 2.
In this embodiment, the condition that the number of fingerprints in region R1 is not equal to the number of fingerprints in region R2 may be characterized as the finger FG being likely to be replaced between the first point in time and the second point in time. Therefore, in step S340, when the number of fingerprint features in the area R1 is determined not to be equal to the number of fingerprint features in the area R2, the comparison module 120 determines that the finger FG is a fake finger in step S330.
For example, the details of the steps S330 and S340 are described, please refer to fig. 1, fig. 3, and fig. 6. FIG. 6 is a diagram illustrating a second fingerprint image of a fake finger according to an embodiment of the present invention. In the present embodiment, compared with the region R1 of the first fingerprint image IM1 shown in fig. 3, the fingerprint feature F6 is added to the region R2 of the second fingerprint image IM2 shown in fig. 6. Therefore, the comparison module 120 can determine that the finger corresponding to the second fingerprint image IM2 is different from the finger FG corresponding to the first fingerprint image IM 1. As such, the comparison module 120 may determine that the finger FG may be replaced between the first time point and the second time point. Therefore, the finger FG is determined as a fake finger.
Returning to the embodiments of fig. 1, 3, and 5, in step S340, the condition that the number of fingerprint features in region R1 is equal to the number of fingerprint features in region R2 may be characterized as the finger FG not being replaced at the first time point and the second time point. Therefore, when the number of fingerprint features in the region R1 is determined to be equal to the number of fingerprint features in the region R2, the comparing module 120 proceeds to step S350.
In step S350, the comparison module 120 determines whether the fingerprint texture correlation in the region R1 of the first fingerprint image IM1 is equal to or similar to the fingerprint texture correlation in the region R2 of the second fingerprint image IM 2. In the present embodiment, the fingerprint ridge correlation in the region R1 is related to the arrangement of the fingerprint features in the region R1. The fingerprint ridge correlation in region R2 is related to the arrangement of fingerprint features in region R2. In this embodiment, a condition in which the correlation of fingerprint grain in region R1 is different from the correlation of fingerprint grain in region R2 may be characterized as a possible change in finger FG between the first and second points in time. Therefore, in step S350, when the fingerprint correlation in the region R1 is determined not to be equal to the fingerprint correlation in the region R2, the comparison module 120 determines that the finger FG is a fake finger in step S330.
For example, the details of the steps S330 and S350 are described, please refer to fig. 1, fig. 3, and fig. 7. FIG. 7 is a diagram illustrating a second fingerprint image of a fake finger according to an embodiment of the present invention. In the present embodiment, the arrangement of the fingerprint features F1 to F3 in the region R2 of the second fingerprint image IM2 shown in fig. 7 is significantly different from the arrangement of the fingerprint features F1, F2, and F3 in the region R1 of the first fingerprint image IM1 shown in fig. 3 (for example, the relative positions of the fingerprint features F1 to F3 are significantly changed or the positions of the fingerprint features F1 to F3 are interchanged). Therefore, the comparison module 120 can determine that the finger corresponding to the second fingerprint image IM2 is different from the finger FG corresponding to the first fingerprint image IM 1. In this way, the comparison module 120 may determine that the finger FG may be replaced between the first time point and the second time point, thereby increasing the accuracy of determining the real finger.
Referring back to the embodiments of fig. 1, 3 and 5, in step S350, the condition that the fingerprint ridge correlation in the region R1 is the same as or similar to the fingerprint ridge correlation in the region R2 may be characterized as that the finger FG has not been replaced between the first time point and the second time point. Therefore, when the fingerprint correlation in the region R1 is determined to be the same as or similar to the fingerprint correlation in the region R2, the comparing module 120 proceeds to step S360.
The sequence of steps S320, S340, and S350 in this embodiment is changed, and is not limited to the sequence of steps S320, S340, and S350 shown in fig. 5.
In step S360, the comparing module 120 determines that the finger FG is a real finger. In the case that the finger FG is determined to be a real finger, the comparison module 120 may further identify the identity of the user corresponding to the finger FG in step S370.
In this embodiment, based on steps S320, S340, and S350, the comparing module 120 determines that the finger FG is a real finger when the following conditions are satisfied: (1) the number of fingerprint features of the second fingerprint image IM2 is greater than that of the first fingerprint image IM 1; (2) the number of fingerprint features in region R1 is equal to the number of fingerprint features in region R2; and (3) the fingerprint ridge correlation in region R1 is determined to be the same or similar to the fingerprint ridge correlation in region R2.
In some embodiments, the comparing module 120 may only perform the determination of steps S320 and S340. That is, when the region R2 has no new fingerprint feature and the region R3 has new fingerprint feature, the comparison module 120 determines that the finger FG is a real finger. Further, in some embodiments, when the number of fingerprint features in region R2 is equal to the number of fingerprint features in region R1 and there is an increase in the number of new fingerprint features in region R3, the comparison module 120 determines that the finger FG is a real finger.
In some embodiments, the comparing module 120 may only perform the determination of steps S320 and S350. That is, when the second fingerprint image IM2 has a new fingerprint feature and the correlation of fingerprint patterns in the region R1 is determined to be the same as or similar to the correlation of fingerprint patterns in the region R2, the comparison module 120 determines that the finger FG is a real finger.
Referring to fig. 1 and 8, fig. 8 is a flowchart illustrating a fingerprint sensing method according to a fourth embodiment of the invention. In the present embodiment, the fingerprint sensing method S400 is applied to the fingerprint sensing device 100. The sensor 110 acquires the first fingerprint image IM1 and the second fingerprint image IM2 of the finger FG at a first time point and a second time point, respectively, and acquires the third fingerprint image at a time point between the first time point and the second time point in step S410. That is, in step S410, the sensor 110 acquires the first fingerprint image IM1 of the finger FG at a first point in time, acquires the third fingerprint image at a point in time between the first point in time and the second point in time, and acquires the second fingerprint image IM2 at the second point in time in step S410.
In step S420, the comparison module 120 compares the number of fingerprint features of the first fingerprint image IM1, the number of fingerprint features of the third fingerprint image and the number of fingerprint features of the second fingerprint image IM 2. In step S420, when the number of fingerprint features of the third fingerprint image is greater than that of the first fingerprint image IM1, and the number of fingerprint features of the second fingerprint image IM2 is greater than that of the third fingerprint image, the comparison module 120 determines in step S430 that the finger FG is an authentic finger. In the case that the finger FG is determined to be a real finger, the comparison module 120 may further identify the identity of the user corresponding to the finger FG in step S440.
On the other hand, when the number of fingerprint features of the third fingerprint image is less than or equal to the number of fingerprint features of the first fingerprint image IM1, or the number of fingerprint features of the second fingerprint image IM2 is less than or equal to the number of fingerprint features of the third fingerprint image, the comparison module 120 determines in step S450 that the finger FG is a fake finger.
In some embodiments, in step S410, the sensor 110 may acquire a plurality of third fingerprint images at a plurality of time points between the first time point and the second time point. Therefore, in step S420, the comparison module 120 compares the number of fingerprint features of the first fingerprint image IM1, the number of fingerprint features of the plurality of third fingerprint images, and the number of fingerprint features of the second fingerprint image IM 2. The time interval for acquiring the plurality of third fingerprint images may be, for example, 0.01 second (the present invention is not limited thereto).
In some embodiments, the comparison module 120 further determines whether the contrast of the fingerprint features of the first fingerprint image IM1, the contrast of the fingerprint features of the second fingerprint image IM2, and the contrast of the fingerprint features of the third fingerprint image are greater than the default values. When the contrast of at least one of the fingerprint features of the first fingerprint image IM1, the fingerprint features of the second fingerprint image IM2, and the fingerprint features of the third fingerprint image is less than or equal to the default value, the comparison module 120 determines that the finger FG is a fake finger.
In summary, the fingerprint sensing device and the fingerprint sensing method of the present invention can acquire the first fingerprint image and the second fingerprint image at different time points, and compare the first fingerprint image and the second fingerprint image according to at least one determination criterion, thereby determining whether the finger is a real finger. Thus, the anti-counterfeiting effect of fingerprint sensing can be improved. In addition, the fingerprint sensing device and the fingerprint sensing method can also judge whether the finger is replaced between the first time point and the second time point according to the fingerprint feature quantity in the first area, the fingerprint feature quantity in the second area and the fingerprint line relevance, so that the anti-counterfeiting effect of fingerprint sensing is further improved.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (23)
1. A fingerprint sensing device for identifying a fingerprint of a finger, the fingerprint sensing device comprising:
the sensor is used for respectively acquiring a first fingerprint image and a second fingerprint image at a first time point and a second time point; and
and the comparison module is coupled to the sensor and compares the first fingerprint image with the second fingerprint image according to at least one judgment standard so as to judge whether the finger is a real finger.
2. The fingerprint sensing device according to claim 1, wherein the at least one criterion is at least one of an area of a fingerprint image, a number of fingerprint features, and a correlation of fingerprint patterns.
3. The fingerprint sensing device according to claim 1, wherein the time interval between the second point in time and the first point in time is less than or equal to 0.5 seconds.
4. The fingerprint sensing device according to claim 1, wherein the comparison module is configured to identify an identity corresponding to the fingerprint upon determining that the finger is a genuine finger.
5. The fingerprint sensing device according to claim 1, wherein the comparison module is configured to determine that the finger is a real finger when the number of fingerprint features of the second fingerprint image is determined to be greater than the number of fingerprint features of the first fingerprint image.
6. The fingerprint sensing device according to claim 1, wherein the alignment module is configured to define a first region corresponding to the first fingerprint image, a second region similar to the first region in the second fingerprint image, and a third region outside the second region.
7. The fingerprint sensing device according to claim 6, wherein the comparison module is configured to determine that the finger is a real finger when the third area has the added fingerprint features and the second area does not have the added fingerprint features.
8. The fingerprint sensing device according to claim 6, wherein the comparison module is configured to determine that the finger is a real finger when the number of fingerprint features in the second area is equal to the number of fingerprint features in the first area and at least one new fingerprint feature is added in the third area.
9. The fingerprint sensing device according to claim 6, wherein the plurality of fingerprint features in the first region have a first fingerprint correlation therebetween, the plurality of fingerprint features in the second region have a second fingerprint correlation therebetween, and the comparison module determines whether the first region is similar to the second region according to the first fingerprint correlation and the second fingerprint correlation.
10. The fingerprint sensing device according to claim 6, wherein the comparison module is further configured to determine that the finger is a fake finger when the second area has new fingerprint features compared to the first area.
11. The fingerprint sensing device according to claim 1, wherein:
the sensor is configured to acquire at least a third fingerprint image at least one time point between the first time point and the second time point, and
the comparison module is configured to determine that the finger is a real finger when the number of fingerprint features of the second fingerprint image is greater than the number of fingerprint features of the at least one third fingerprint image and the number of fingerprint features of the at least one third fingerprint image is greater than the number of fingerprint features of the first fingerprint image.
12. The fingerprint sensing device according to claim 1, wherein the contrast of the fingerprint features of the first fingerprint image, the contrast of the fingerprint features of the second fingerprint image and the contrast of the fingerprint features of the at least one third fingerprint image are greater than a predetermined value.
13. A fingerprint sensing method, wherein the fingerprint sensing method is applied to a fingerprint sensing apparatus, the fingerprint sensing apparatus includes a sensor and a comparison module, the fingerprint sensing method includes:
respectively acquiring a first fingerprint image and a second fingerprint image at a first time point and a second time point by the sensor; and
and the comparison module compares the first fingerprint image with the second fingerprint image according to at least one judgment standard so as to judge whether the finger is a real finger.
14. The fingerprint sensing method according to claim 13, wherein the at least one criterion is at least one of area of fingerprint image, number of fingerprint features, and correlation of fingerprint patterns.
15. The fingerprint sensing method of claim 13, wherein a time interval between the second point in time and the first point in time is less than or equal to 0.5 seconds.
16. The fingerprint sensing method according to claim 13, further comprising:
and identifying the identity corresponding to the fingerprint after the comparison module judges that the finger is a real finger.
17. The fingerprint sensing method according to claim 13, wherein the step of comparing the first fingerprint image and the second fingerprint image according to the at least one determination criterion to determine whether the finger is a real finger comprises:
and when the fingerprint feature quantity of the second fingerprint image is judged to be larger than that of the first fingerprint image, the comparison module judges that the finger is a real finger.
18. The fingerprint sensing method according to claim 13, further comprising:
defining, by the comparison module, a first region corresponding to the first fingerprint image; and
and defining a second area similar to the first area and a third area outside the second area in the second fingerprint image by the comparison module.
19. The fingerprint sensing method according to claim 18, wherein the step of comparing the first fingerprint image and the second fingerprint image by the comparison module according to the at least one determination criterion to determine whether the finger is a real finger further comprises:
and when the third area has the added fingerprint features and the second area does not have the added fingerprint features, the comparison module judges that the finger is a real finger.
20. The fingerprint sensing method according to claim 18, wherein the step of comparing the first fingerprint image and the second fingerprint image by the comparison module according to the at least one determination criterion to determine whether the finger is a real finger further comprises:
when the number of the fingerprint features in the second area is equal to the number of the fingerprint features in the first area and at least one new fingerprint feature is added in the third area, the comparison module judges that the finger is a real finger.
21. The method of claim 18, wherein the first region has a first fingerprint correlation between the fingerprint features and the second region has a second fingerprint correlation between the fingerprint features, and the comparing module compares the first fingerprint image and the second fingerprint image according to the at least one determination criterion to determine whether the finger is a real finger further comprises:
and judging whether the first region is similar to the second region or not by the comparison module according to the correlation of the first fingerprint texture and the correlation of the second fingerprint texture.
22. The fingerprint sensing method according to claim 18, wherein the step of comparing the first fingerprint image and the second fingerprint image by the comparison module according to the at least one determination criterion to determine whether the finger is a real finger comprises:
and when the second area has the newly added fingerprint characteristics compared with the first area, the comparison module judges that the finger is a fake finger.
23. The fingerprint sensing method according to claim 13, wherein:
the step of acquiring, by the sensor, the first fingerprint image and the second fingerprint image at the first time point and the second time point, respectively, comprises:
acquiring, by the sensor, at least a third fingerprint image at least one point in time between the first point in time and the second point in time, and
comparing the first fingerprint image and the second fingerprint image by the comparison module according to the at least one judgment standard to judge whether the finger is a real finger or not, wherein the step of comparing the first fingerprint image and the second fingerprint image by the comparison module comprises the following steps:
and when the number of the fingerprint features of the second fingerprint image is greater than that of the fingerprint features of the at least one third fingerprint image, and the number of the fingerprint features of the at least one third fingerprint image is greater than that of the fingerprint features of the first fingerprint image, the comparison module judges that the finger is a real finger.
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| CN112464866B (en) | 2024-02-27 |
| TWI766474B (en) | 2022-06-01 |
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