CN105260720A - Fingerprint identification method and device - Google Patents
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- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 9
- 239000004744 fabric Substances 0.000 description 4
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- 239000011159 matrix material Substances 0.000 description 2
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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
- G06V40/1365—Matching; Classification
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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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- 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 embodiment of the invention discloses a fingerprint identification method and device. The method comprises: obtaining a target image from a captured texture image; determining a row variance according to the pixel value of each row of pixel points in the target image, and/or, determining a column variance according to the pixel value of each column of pixel points in the target image; and determining the captured texture image to be a fingerprint image if the row variance and/or the column variance is greater than a preset variance. The method and device can determine whether the captured texture image is a fingerprint image before fingerprint identification, and perform fingerprint identification when the captured texture image is determined to be a fingerprint image, thereby reducing unnecessary fingerprint identification, and improving system resource utilization and identification efficiency.
Description
Technical field
The embodiment of the present invention relates to electronic apparatus application technology, particularly relates to a kind of method and device of fingerprint recognition.
Background technology
Along with the development of electronic equipment, fingerprint identification technology is widely applied in intelligent terminal.User carries out the operations such as unblock by fingerprint recognition to intelligent terminal.
In prior art, capacitive fingerprint sensing device is used to carry out fingerprint recognition.Because human body is conductor, therefore when finger presses capacitive fingerprint sensing device, fingerprint sensor can obtain the texture of finger, and then carries out follow-up fingerprint recognition operation according to this texture.
When intelligent terminal is put in pocket, the fabric texture on pocket, by being caught by the fingerprint sensor in intelligent terminal and identifying, causes unnecessary identification, waste system resource.
Summary of the invention
The invention provides a kind of method and device of fingerprint recognition, effectively identifying with the image realized catching, improve the resource utilization of intelligent terminal.
First aspect, embodiments provides a kind of method of fingerprint recognition, comprising:
Target image is obtained from the texture image of catching;
According in described target image often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance;
If described row variance and/or described row variance are greater than default variance, then determine that described texture image is fingerprint image.
Second aspect, the embodiment of the present invention additionally provides a kind of device of fingerprint recognition, comprising:
Target image acquiring unit, for obtaining target image, described target image is contained in texture image;
Variance computing unit, for in the described target image that obtains according to described target image acquiring unit often the pixel value of row pixel determine row variance, and/or, according in the described target image that described target image acquiring unit obtains often the pixel value of row pixel determine row variance;
Determining unit, if the described row variance obtained for described variance computing unit and/or row variance are greater than default variance, then determines that described texture image is fingerprint image.
The present invention, by before carrying out fingerprint recognition, obtains the row variance in texture image in target image and/or row variance, when row variance and/or row variance are greater than default variance, determines that texture image is fingerprint image, and start follow-up fingerprint recognition flow process.Directly carry out compared with fingerprint recognition to the texture image obtained with prior art, the present invention can judge before carrying out fingerprint recognition whether the texture image obtained is fingerprint image, when being defined as fingerprint image, carrying out fingerprint recognition, reduce unnecessary fingerprint recognition, improve utilization factor and the recognition efficiency of system resource.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the method for a fingerprint recognition in the embodiment of the present invention one;
Fig. 2 is the coordinate schematic diagram of a texture image in the embodiment of the present invention one;
Fig. 3 is the process flow diagram of the method for first fingerprint recognition in the embodiment of the present invention two;
Fig. 4 is the process flow diagram of the method for second fingerprint recognition in the embodiment of the present invention two;
Fig. 5 is the division schematic diagram of a texture image in the embodiment of the present invention two;
Fig. 6 is the division schematic diagram of another texture image in the embodiment of the present invention two;
Fig. 7 is the process flow diagram of the method for the 3rd fingerprint recognition in the embodiment of the present invention two;
Fig. 8 is the position view of the preset position area in the embodiment of the present invention two;
Fig. 9 is the process flow diagram of the method for the 4th fingerprint recognition in the embodiment of the present invention two;
Figure 10 is the structural representation of the device of first fingerprint recognition in the embodiment of the present invention three;
Figure 11 is the structural representation of the device of second fingerprint recognition in the embodiment of the present invention three;
Figure 12 is the structural representation of the device of the 3rd fingerprint recognition in the embodiment of the present invention three.
Embodiment
Below in conjunction with drawings and Examples, the present invention is described in further detail.Be understandable that, specific embodiment described herein is only for explaining the present invention, but not limitation of the invention.It also should be noted that, for convenience of description, illustrate only part related to the present invention in accompanying drawing but not entire infrastructure.
Embodiment one
The process flow diagram of the method for the fingerprint recognition that Fig. 1 provides for the embodiment of the present invention one, the present embodiment is applicable to the situation of being carried out fingerprint recognition by intelligent terminal, the method can be performed by the intelligent terminal with fingerprint identification function, intelligent terminal is as smart mobile phone, panel computer etc., and the method specifically comprises the steps:
Step 110, from the texture image of catching, obtain target image.
Intelligent terminal obtains texture image by fingerprint sensor.Texture image can be gray-scale map.Target image can be texture image, also can be the subimage in texture image.
Step 120, according in target image often the pixel value of row pixel determine row variance, and/or, according in target image often the pixel value of row pixel determine row variance.
Specifically implement by following manner:
Calculate the pixel value sum of often row pixel in described target image, the pixel value sum according to described often row pixel determines row variance; And/or calculate the pixel value sum of often row pixel in described target image, the pixel value sum according to described often row pixel determines row variance.
In gray-scale map, the color of each pixel is represented by RGB RGB tlv triple.Conveniently calculate, (R, G, B) tlv triple of each pixel obtained gray-scale value (i.e. pixel value) Gray corresponding with (R, G, B) tlv triple by any one conversion regime following:
Mode one: floating-point arithmetic: Gray=R × 0.3+G × 0.59+B × 0.11
Mode two: integer method: Gray=(R × 30+G × 59+B × 11) ÷ 100
Mode three: mean value method: Gray=(R+G+B) ÷ 3
Mode four: only get green: Gray=G
Gray-scale value corresponding to pixel can be obtained, i.e. the pixel value of pixel by any one mode above-mentioned.The corresponding pixel of each coordinate points in texture image, each pixel has unique pixel value, as gray-scale value Gray.For convenience of description, adopt the texture image coordinate shown in Fig. 3 to be described in the present embodiment and subsequent embodiment, as shown in Figure 2, the pixel matrix that texture image is arranged by the capable n of m forms, and comprises m × n pixel altogether, is positioned at the pixel (x of the capable n row of m
n, y
m) corresponding pixel value is G
nm.Optionally, m=n=480.
Determine in the implementation of row variance a kind of, with behavior unit, respectively calculate be positioned at the pixel value of the pixel of same a line and.First whole the pixel [(x in the first row are obtained
1, y
1), (x
2, y
1) ... (x
n, y
1)] and pixel value [G
11, G
12g
1n], and calculate each pixel pixel value of the first row [G
11, G
12g
1n] and A
1.Then, whole the pixel [(x in the second row are obtained
1, y
2), (x
2, y
2) ... (x
n, y
2)] and pixel value [G
21, G
22g
2n], and calculate each pixel pixel value of the second row [G
21, G
22g
2n] and A
2.By that analogy, the third line is obtained to the capable each pixel pixel value [A of m
3, A
4a
m].
When calculating row variance, first according to formula one calculate every row pixel and mean value M.Then formula one is obtained this mean value M to be updated in formula two and to calculate row variance.
Formula one:
Wherein, M be often row pixel and mean value, A
1to A
msuccessively represent the first row to the capable pixel pixel value of often going of m and.
Formula two:
Wherein, H
2for row variance, A
1to A
msuccessively represent the first row to the capable pixel pixel value of often going of m and.
Determine in the implementation of row variance a kind of, to arrange as unit, respectively calculate be positioned at the pixel value of the pixel of same row and.First whole the pixel [(x in first row are obtained
1, y
1), (x
1, y
2) ... (x
1, y
m)] and pixel value [G
11, G
21g
m1], and calculate each pixel pixel value of first row [G
11, G
21g
m1] and B
1.Then, whole the pixel [(x in secondary series are obtained
2, y
1), (x
2, y
2) ... (x
2, y
m)] and pixel value [G
12, G
22g
m2], and calculate each pixel pixel value of secondary series [G
12, G
22g
m2] and B
2.By that analogy, the 3rd row are obtained to each pixel pixel value [B of the n-th row
3, B
4b
n].
When calculated column variance, first according to formula three calculate every row pixel and mean value N.Then formula one is obtained this mean value N and be updated to calculated column variance in formula four.
Formula three:
Wherein, N be often row pixel and mean value, A
1to A
msuccessively represent first row arrange to m the pixel pixel value often arranged with.
Formula four:
Wherein, L
2for row variance, B
1to B
nsuccessively represent first row arrange to m the pixel pixel value often arranged with.
If step 130 row variance and/or row variance are greater than default variance, then determine that texture image is fingerprint image.
When texture image is fingerprint image, owing to there is the gap between irregular texture and texture in fingerprint image, and have compared with rule texture pocket fabric, the variance that fingerprint image calculates will be greater than the variance of the pocket fabric of regular veins.Default variance yields can define with reference to the variance yields that regular figure is corresponding, and the pixel value of such as variance yields is 0-10, is preferably 6.
The technical scheme that the present embodiment provides is by before carrying out fingerprint recognition, obtain the row variance in texture image in target image and/or row variance, when row variance and/or row variance are greater than default variance, determine that texture image is fingerprint image, and start follow-up fingerprint recognition flow process.Directly carry out compared with fingerprint recognition to the texture image obtained with prior art, the present embodiment can judge before carrying out fingerprint recognition whether the texture image obtained is fingerprint image, when being defined as fingerprint image, carrying out fingerprint recognition, reduce unnecessary fingerprint recognition, improve utilization factor and the recognition efficiency of system resource.
In addition, in prior art, mobile phone is except can contact with the fabric in pocket, also may with the skin contact at other positions of human body, such as palm, face, nose etc.Because the dermatoglyph of above-mentioned human body is comparatively even, the row variance therefore calculated and/or row variance are still less than row variance corresponding to fingerprint and/or row variance.Therefore, above-described embodiment also can be avoided causing false touch to make intelligent terminal carry out fingerprint recognition with contact human skin, improves recognition efficiency.
Embodiment two
The embodiment of the present invention additionally provides a kind of method of fingerprint recognition, and as further illustrating above-described embodiment, as shown in Figure 3, after step 110, acquisition target image, described method also comprises:
Step 150, binary conversion treatment is carried out to target image.
Setting threshold value T, is divided into two parts with threshold value T by the pixel of target image: pixel value is greater than the pixel group that the pixel group of threshold value T and pixel value are less than threshold value T.Pixel value pixel value being greater than the pixel group of threshold value T is set as white (or black), and the pixel value that pixel value is less than the pixel group of threshold value T is set as black (or white).After binary conversion treatment, the pixel value of the pixel in target image is 0 or 1.If target image is fingerprint image, then the pixel value of the pixel that the texture of fingerprint is corresponding is 1 (or being 0), and the pixel value of the pixel that the gap between fingerprint texture is corresponding is 0 (or being 1).When target image is gray-scale map, the span of the gray-scale value (i.e. pixel value) that threshold value T is corresponding is 0-255, and the value of exemplary threshold value T is 120.
Accordingly, step 120, according in target image often the pixel value of row pixel determine row variance, and/or, according in target image often the pixel value of row pixel determine row variance, implement by following manner:
Step 120a, according in the target image of binaryzation often the pixel value of row pixel determine row variance, and/or, according in target image often the pixel value of row pixel determine row variance.
If the matrix image of target image to be m capable n row, then in the target image of binaryzation, often the pixel value sum of row pixel is n to the maximum, and often the pixel value sum of row pixel is m to the maximum.
The technical scheme that the present embodiment provides, can carry out binary conversion treatment by target image, obtain bianry image.Pixel value due to pixel in bianry image is 0 or is 1, therefore, it is possible to reduce the pixel value sum of often row pixel and the numerical value of the every pixel value sum of row pixel, and then reduce the complexity calculating variance in step 130, improve the speed that variance calculates, and then improve the recognition efficiency of picture.
The embodiment of the present invention additionally provides a kind of method of fingerprint recognition, as further illustrating above-described embodiment, as shown in Figure 4, step 110, from the texture image of catching, obtains target image, comprising:
Step 111, from texture image, determine at least one target area.
Wherein, the area of target area is less than the area of texture image.When dividing, texture image can be divided into multiple target area.Such as: as shown in Figure 5, from midline position, texture image is divided into the first half and Lower Half, and the first half is wherein defined as target area.Again such as: as shown in Figure 6, texture image is divided into upper left quarter, lower left quarter, upper right quarter, right lower quadrant four parts, and upper left quarter wherein and right lower quadrant are defined as target area.
Also target area can be determined from texture image.Such as: the precalculated position in texture image, by [(a
1, b
1), (a
2, b
2), (a
3, b
3), (a
4, b
4)] the rectangular coordinates region that forms is defined as target area.
Step 112, the image in each target area is defined as target image respectively.
Accordingly, if step 130 row variance and/or row variance are greater than default variance, then determine that texture image is fingerprint image, implement by following manner:
If the row variance that each target image of step 130a is corresponding and/or row variance are all greater than default variance, then determine that texture image is fingerprint image.
When determining multiple target area, calculate the row variance (or row variance) in each region respectively.If the row variance of the plurality of target area (or row variance) is all greater than default variance, then illustrate that the image in the plurality of target area is irregular image, and then determine that texture image is fingerprint image.
The technical scheme that the present embodiment provides, when determining a target area from texture image, the area due to target area is less than the area of texture image, therefore, it is possible to reduce calculated amount, improves the computing velocity of variance, improves the recognition speed of fingerprint image.When determining multiple target area from texture image, can on the basis of recognition speed of improving fingerprint image, improving identification is accuracy.
The embodiment of the present invention additionally provides a kind of method of fingerprint recognition, as further illustrating above-described embodiment, as shown in Figure 7, step 111, from texture image, determines at least one target area, also implements by following manner:
Step 111 ＇, the preset position area in texture image is defined as target area, preset position area and texture image have identical geometric center.
When texture image is fingerprint image, the central area of fingerprint image is the image of gap composition between fingerprint texture and texture.Image due to this central area more adequately can embody the grain distribution feature of fingerprint image, therefore the preset position area of central area is defined as target area.The size of preset position area can be determined according to the specified identification range of fingerprint sensor.Optionally, as shown in Figure 8, the length of preset position area is 1/2nd of fingerprint sensor length and the width of preset position area is 1/2nd of fingerprint sensor width, and the diagonal line intersection point of preset position area overlaps with the diagonal line intersection point of texture image.
The technical scheme that the present embodiment provides, the target area that the feature that can take the fingerprint from texture image is easier to distinguish, improves the accuracy identified while reducing calculated amount.
Provide an optional manner of above-described embodiment below by a use scenes, as shown in Figure 9, comprising:
Step 110, from the texture image of catching, obtain target image.
Step 120b, according in described target image often the pixel value of row pixel determine row variance.
Step 120c, according in described target image often the pixel value of row pixel determine row variance.
If step 130b row variance and row variance are all greater than default variance, then determine that texture image is fingerprint image.
When variance of being expert in above-mentioned use scenes and row variance are all greater than default variance, determine that texture image is fingerprint image.With only determine compared with fingerprint image, jointly to determine whether as fingerprint image by row variance and row variance, the recognition accuracy of fingerprint image can be improved further according to row variance (or row variance).
Embodiment three
The embodiment of the present invention additionally provides a kind of device 1 of fingerprint recognition, and this device 1 is for implementing the method shown in above-described embodiment and being arranged in intelligent terminal, and as shown in Figure 10, this device 1 comprises:
Target image acquiring unit 11, for obtaining target image, described target image is contained in texture image;
Variance computing unit 12, for in the described target image that obtains according to described target image acquiring unit 11 often the pixel value of row pixel determine row variance, and/or, according in the described target image that described target image acquiring unit 11 obtains often the pixel value of row pixel determine row variance;
Determining unit 13, if the described row variance obtained for described variance computing unit 12 and/or row variance are greater than default variance, then determines that described texture image is fingerprint image.
Further, described variance computing unit 12 for:
Calculate the pixel value sum of often row pixel in the described target image of described target image acquiring unit 11 acquisition, the pixel value sum according to described often row pixel determines row variance; And/or,
Calculate the pixel value sum of often row pixel in the described target image of described target image acquiring unit 11 acquisition, the pixel value sum according to described often row pixel determines row variance.
Further, as shown in figure 11, described device also comprises:
Two-value processing unit 14, carries out binary conversion treatment for the described target image obtained described target image acquiring unit 11;
Described variance computing unit 12 also for, according in the target image of described two-value processing unit 14 binaryzation often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance.
Further, as shown in figure 12, described target image acquiring unit 11, comprising:
Subelement 111 is determined in target area, for determining at least one target area from described texture image;
Target image determination subelement 112, for determining that by described target area the image in each target area that subelement 111 marks off is defined as target image respectively;
Described determining unit 13 also for, if row variance corresponding to each described target image determined of described target image determination subelement 112 and/or row variance are all greater than described default variance, then determine that described texture image is fingerprint image.
Further, described target area determine subelement 111 also for, the preset position area in described texture image is defined as described target area, and described preset position area has identical geometric center with described texture image.
Said apparatus can perform the method that the embodiment of the present invention one and embodiment two provide, and possesses and performs the corresponding functional module of said method and beneficial effect.The not ins and outs of detailed description in the present embodiment, the method that can provide see the embodiment of the present invention one and embodiment two.
In each embodiment of the present invention, term "and/or" is only a kind of incidence relation describing affiliated partner, and expression can exist three kinds of relations.Such as, A and/or B, can represent: individualism A, exists A and B simultaneously, these three kinds of situations of individualism B.In addition, character "/" herein, general expression forward-backward correlation is to the relation liking a kind of "or".
Note, above are only preferred embodiment of the present invention and institute's application technology principle.Skilled person in the art will appreciate that and the invention is not restricted to specific embodiment described here, various obvious change can be carried out for a person skilled in the art, readjust and substitute and can not protection scope of the present invention be departed from.Therefore, although be described in further detail invention has been by above embodiment, the present invention is not limited only to above embodiment, when not departing from the present invention's design, can also comprise other Equivalent embodiments more, and scope of the present invention is determined by appended right.
Claims (10)
1. a method for fingerprint recognition, is characterized in that, comprising:
Target image is obtained from the texture image of catching;
According in described target image often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance;
If described row variance and/or described row variance are greater than default variance, then determine that described texture image is fingerprint image.
2. the method for fingerprint recognition according to claim 1, is characterized in that, described according in described target image often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance, comprising:
Calculate the pixel value sum of often row pixel in described target image, the pixel value sum according to described often row pixel determines row variance; And/or,
Calculate the pixel value sum of often row pixel in described target image, the pixel value sum according to described often row pixel determines row variance.
3. the method for fingerprint recognition according to claim 1, is characterized in that, after obtain target image from the texture image of catching, described method also comprises:
Binary conversion treatment is carried out to described target image;
Accordingly, described according in described target image often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance, comprising:
According in the target image of binaryzation often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance.
4. the method for fingerprint recognition according to claim 1, is characterized in that, obtaining target image, comprising the described texture image from catching:
At least one target area is determined from described texture image;
Image in each target area is defined as target image respectively;
Accordingly, if described row variance and/or described row variance are greater than default variance, then determine that described texture image is fingerprint image, comprising:
If the row variance that each described target image is corresponding and/or row variance are all greater than described default variance, then determine that described texture image is fingerprint image.
5. the method for fingerprint recognition according to claim 4, is characterized in that, describedly from described texture image, determines at least one target area, comprising:
Preset position area in described texture image is defined as described target area, and described preset position area has identical geometric center with described texture image.
6. a device for fingerprint recognition, is characterized in that, comprising:
Target image acquiring unit, for obtaining target image, described target image is contained in texture image;
Variance computing unit, for in the described target image that obtains according to described target image acquiring unit often the pixel value of row pixel determine row variance, and/or, according in the described target image that described target image acquiring unit obtains often the pixel value of row pixel determine row variance;
Determining unit, if the described row variance obtained for described variance computing unit and/or row variance are greater than default variance, then determines that described texture image is fingerprint image.
7. the device of fingerprint recognition according to claim 6, is characterized in that, described variance computing unit is used for:
Calculate the pixel value sum of often row pixel in the described target image of described target image acquiring unit acquisition, the pixel value sum according to described often row pixel determines row variance; And/or,
Calculate the pixel value sum of often row pixel in the described target image of described target image acquiring unit acquisition, the pixel value sum according to described often row pixel determines row variance.
8. the device of fingerprint recognition according to claim 6, is characterized in that, described device also comprises:
Two-value processing unit, carries out binary conversion treatment for the described target image obtained described target image acquiring unit;
Described variance computing unit also for, according in the target image of described two-value processing unit binaryzation often the pixel value of row pixel determine row variance, and/or, according in described target image often the pixel value of row pixel determine row variance.
9. the device of fingerprint recognition according to claim 6, is characterized in that, described target image acquiring unit, comprising:
Subelement is determined in target area, for determining at least one target area from described texture image;
Target image determination subelement, for determining that by described target area the image in each target area that subelement marks off is defined as target image respectively;
Described determining unit also for, if row variance corresponding to each described target image determined of described target image determination subelement and/or row variance are all greater than described default variance, then determine that described texture image is fingerprint image.
10. the device of fingerprint recognition according to claim 8, it is characterized in that, described target area determine subelement also for, the preset position area in described texture image is defined as described target area, and described preset position area has identical geometric center with described texture image.
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|---|---|---|---|
| CN201510679995.3A CN105260720B (en) | 2015-10-19 | 2015-10-19 | fingerprint identification method and device |
| PCT/CN2016/093746 WO2017067287A1 (en) | 2015-10-19 | 2016-08-05 | Fingerprint recognition method, apparatus, and terminal |
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|---|---|---|---|
| CN201510679995.3A CN105260720B (en) | 2015-10-19 | 2015-10-19 | fingerprint identification method and device |
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| CN107895143A (en) * | 2017-10-27 | 2018-04-10 | 维沃移动通信有限公司 | A kind of finger print information processing method, mobile terminal and computer-readable recording medium |
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| CN105260720B (en) * | 2015-10-19 | 2017-02-22 | 广东欧珀移动通信有限公司 | fingerprint identification method and device |
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| CN1716274A (en) * | 2004-07-02 | 2006-01-04 | 电子科技大学 | Finger print image splitting method based on direction information |
| CN1595425A (en) * | 2004-07-13 | 2005-03-16 | 清华大学 | Method for identifying multi-characteristic of fingerprint |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2017067287A1 (en) * | 2015-10-19 | 2017-04-27 | 广东欧珀移动通信有限公司 | Fingerprint recognition method, apparatus, and terminal |
| CN106055947A (en) * | 2016-05-23 | 2016-10-26 | 广东欧珀移动通信有限公司 | Method and apparatus for unlocking mobile terminal through fingerprint, and mobile terminal |
| CN106066685A (en) * | 2016-05-30 | 2016-11-02 | 广东欧珀移动通信有限公司 | A kind of solution lock control method and terminal unit |
| CN106066685B (en) * | 2016-05-30 | 2018-01-19 | 广东欧珀移动通信有限公司 | One kind solution lock control method and terminal device |
| CN107895143A (en) * | 2017-10-27 | 2018-04-10 | 维沃移动通信有限公司 | A kind of finger print information processing method, mobile terminal and computer-readable recording medium |
| CN108731772A (en) * | 2018-03-20 | 2018-11-02 | 丁敏 | Big data measurement method |
| CN112561821A (en) * | 2020-12-17 | 2021-03-26 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Chip surface electromagnetic data noise reduction method based on near field scanning |
| CN112561821B (en) * | 2020-12-17 | 2024-05-17 | 中国电子产品可靠性与环境试验研究所（（工业和信息化部电子第五研究所）（中国赛宝实验室）） | Near field scanning-based chip surface electromagnetic data noise reduction method |
Also Published As
| Publication number | Publication date |
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| CN105260720B (en) | 2017-02-22 |
| WO2017067287A1 (en) | 2017-04-27 |
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