CN113139517A - Face living body model training method, face living body model detection method, storage medium and face living body model detection system - Google Patents
Face living body model training method, face living body model detection method, storage medium and face living body model detection system Download PDFInfo
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Abstract
The invention provides a face living body model training method, a detection method, a storage medium and a detection system, wherein a visible light camera is used for acquiring a photo and calculating the average value of the variance sum of three channel difference values of the photo, and because a near-infrared living body detection model has poor attack prevention capability on black-white and near-infrared photos, the method is used for judging whether the photo is a black-white photo or a near-infrared photo so as to prevent the attack of the photo; the characteristic that near-infrared imaging is not affected by illumination conditions is utilized, only a near-infrared camera is utilized to collect training samples, and a near-infrared living body detection model is trained. The visible light living body detection model does not need to be trained. The method and the system solve the problem of face living body detection under different light rays such as dark light, backlight, strong light, sunshine and the like, the scheme only needs to train a near-infrared face living body detection model, the algorithm speed is improved, and the detection efficiency is improved.
Description
Technical Field
The invention belongs to the field of artificial intelligence living body intelligent detection, relates to a face recognition technology, and particularly relates to a face living body model training method, a face living body model detection method, a face living body model storage medium and a face living body model detection system.
Background
As the face recognition technology is applied to more and more scenes, the safety of face recognition is more and more concerned by people, and one of the key links is face living body detection, that is, whether a current face image is a real living body or a non-real face image, such as a photo, a video, a face mask and the like with a face is judged. The human face living body detection is an important step before the human face recognition, so that the attack of a non-living body can be effectively prevented, and the safety of a human face recognition system is guaranteed.
Patent CN 107358157B discloses a face living body detection method, device and electronic device, which train a first deep learning model through a face global image and train a second deep learning model through a face image cut, and then perform face living body detection using the two models.
Patent CN 107862299 a discloses a living body face detection method based on near-infrared and visible light binocular cameras, which uses the LBP features of near-infrared and visible light to train a living body detection model for preventing the attacks of video and photos.
However, in the two patent technologies, since the living body detection is performed by using the visible light and the near infrared light at the same time, the living body detection effect is good under good illumination conditions, but the visible light living body detection is easily affected by light, especially under illumination conditions such as dark light, backlight, and strong light, the visible light living body detection effect is poor, the experience is very poor in practical use, and a living body of a real person is easily detected as a non-living body. In addition, the visible light and the near infrared light models are used for judging simultaneously, the calculated amount is large, and the speed is slow.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a face living body model training method, a detection method, a storage medium and a detection system, which can solve the problems.
The design principle is as follows:
a. the method is used for judging whether the photo is a black-white photo or a near-infrared photo or not so as to prevent the attack of the photo.
b. The characteristic that near-infrared imaging is not affected by illumination conditions is utilized, only a near-infrared camera is utilized to collect training samples, and a near-infrared living body detection model is trained. The visible light living body detection model does not need to be trained.
The design scheme is as follows:
a near-infrared human face living body detection model training method comprises the following steps:
s11, collecting a human face living body detection training sample by using a near infrared camera in a binocular camera, wherein the collected sample comprises a real human living body sample and a non-living body sample under different illumination conditions;
s12, the collected samples are sorted, a face detector is used for detecting an original image, the detected face is cut, and the cut face picture is used as a training sample; the method comprises the following steps of intercepting a face photo of stored living body data as a positive sample, and intercepting a face of stored non-living body data as a negative sample;
s13, resampling the sample to obtain a resampling image;
s14, carrying out convolution calculation, batch normalization, ReLU activation and maximum pooling calculation on the resampling images;
s15, inputting the result after the maximum pooling calculation into a dense block for calculation;
s16, inputting the result of the dense block calculation into the transition block for calculation;
s17, carrying out convolution operation of 1x1 on the calculated result of the transition block, and then carrying out sigmoid activation to obtain a feature map;
s18, linearizing the characteristic diagram, and activating sigmoid to obtain binary output;
s19, training by using the feature map calculated in the step S7 and the binary output calculated in the step S8 and using a cross entropy loss function BCE for binary classification as a loss function, wherein the loss function is as follows: loss 0.5 Lossmap+0.5*Lossbinary。
The invention also provides a human face living body detection method suitable for different light rays, which comprises the following steps:
s21, collecting images by using a binocular camera, and respectively collecting a visible light image and a near infrared light image;
s22, carrying out face detection on the collected visible light image and near infrared light, if the face cannot be detected, continuously carrying out detection, and if the face is detected, entering the next step;
s23, a: calculating the variance of the difference value of three channels by the visible light face image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2, and the variance of subtracting the difference value of the channel 1 from the channel 3;
b: resampling the near-infrared face image to accord with a near-infrared face living body detection model;
s24, a: calculating the variance average value of the visible light face image, comparing the variance average value with a set variance threshold value, if the variance average value is smaller than the variance threshold value, judging that the picture is a black-and-white or gray-scale picture, and if the variance average value is larger than the variance threshold value, judging that the picture is a color picture;
b: importing a resample image of the near-infrared face image into a trained near-infrared face living body detection model for calculation, and judging and outputting whether the near-infrared face image is detected as a living body;
and S25, carrying out logic AND operation on the visible light image judgment result and the near infrared light image judgment result, wherein when the visible light image is judged to be a color photograph and the near infrared image is judged to be a living body, the detection target is a living body, otherwise, the detection target is a non-living body.
The invention also provides a computer readable storage medium on which computer instructions are stored, which when executed perform the aforementioned biopsy method.
The invention also provides a human face in-vivo detection system suitable for different light rays, which comprises an optical image acquisition device and a computer which are in telecommunication connection, wherein the optical image acquisition device is a binocular camera comprising a visible light lens and a near infrared light lens so as to simultaneously acquire a visible light image and a near infrared image and transmit the two acquired images to the computer, and the computer receives image data of the optical image acquisition device and operates the in-vivo detection method to judge whether a detection target is a living body; the computer includes:
the visible light image pre-judging unit is used for calculating the variance of the difference value of three channels of the received visible light image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2 and the variance of subtracting the difference value of the channel 1 from the channel 3; comparing the calculated variance with a set variance threshold value, and pre-judging whether the photo is a color photo;
the near-infrared image pre-judging unit receives the near-infrared image, firstly re-samples the near-infrared face image, then introduces the re-sampled image into a trained near-infrared face living body detection model for calculation, and pre-judges whether the near-infrared image is detected as a living body;
the living body detection comprehensive judgment unit receives the judgment results of the visible light image pre-judgment unit and the near infrared image pre-judgment unit, performs logic and operation and judges whether a detection target is a living body;
and the result output unit receives the judgment result of the living body detection comprehensive judgment unit and visually displays the real-time judgment result.
Compared with the prior art, the invention has the beneficial effects that: the scheme utilizes the characteristic that near infrared imaging is insensitive to illumination, only uses near infrared light to carry out living body detection, and also utilizes the image characteristic of visible light to calculate the average value of the variance of the difference values of three channels of the image for judging whether the image is a color photo or not. The living body detection method capable of adapting to various illumination conditions is invented by combining the characteristics of visible light and near infrared imaging, and the detection efficiency is improved because only one near infrared living body detection model is used.
Drawings
FIG. 1 is a schematic flow chart of a near-infrared human face in-vivo detection model training method of the present invention;
FIG. 2 is a near infrared image and a visible light image of a normal light sample;
FIG. 3 is a dark light sample near infrared image and a visible light image;
FIG. 4 is a near infrared image and a visible light image of a highlight sample;
FIG. 5 is a near infrared image and a visible light image of a backlit sample;
FIG. 6 shows near-infrared and visible light images of a yin-yang light sample;
FIG. 7 is a schematic diagram of capturing a face picture;
FIG. 8 is a schematic flow chart of a human face in-vivo detection method adapted to different light rays;
fig. 9 is a schematic diagram of a human face living body detection system adapted to different light rays.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be understood that "system", "device", "unit" and/or "module" as used in this specification is a method for distinguishing different components, elements, parts or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
First embodiment
A training method of a near-infrared human face living body detection model is disclosed, referring to fig. 1, and comprises the following steps:
s11, collecting a human face living body detection training sample by using a near infrared camera in a binocular camera, wherein the collected sample comprises a real human living body sample and a non-living body sample under different illumination conditions; wherein, the different illumination conditions comprise normal light, dark light, strong light, backlight and sunshine. Referring to fig. 2-6, fig. 2a is a normal light sample of a near-infrared light image, and fig. 2b is a normal light sample of a visible light image; FIG. 3a is a dim sample of a near infrared image, and FIG. 3b is a dim sample of a visible image; FIG. 4a is a highlight sample for a near-infrared image, and FIG. 4b is a highlight sample for a visible image; FIG. 5a is a backlight sample of a near-infrared image, and FIG. 5b is a backlight sample of a visible image; fig. 6a is a yin-yang light sample of a near-infrared light image, and fig. 6b is a yin-yang light sample of a visible light image.
S12, the collected samples are sorted, a face detector is used for detecting an original image, the detected face is cut, referring to the picture in figure 7, and the cut face picture is used as a training sample; the method comprises the following steps of intercepting a face photo of stored living body data as a positive sample, and intercepting a face of stored non-living body data as a negative sample;
s13, resampling the sample to obtain a resampling image; the resample size is 112 × 112px (in pixel size), although the above sizes are only exemplary, and other sizes commonly used in the art may be used as the size protection range of the resample map as long as the subsequent processing is facilitated.
S14, carrying out convolution calculation, batch normalization, ReLU activation and maximum pooling calculation on the resampling images;
s15, inputting the result after the maximum pooling calculation into a dense block for calculation;
s16, inputting the result of the dense block calculation into the transition block for calculation;
s17, carrying out convolution operation of 1x1 on the calculated result of the transition block, and then carrying out sigmoid activation to obtain a feature map; the size of the feature map is 7 × 7px (expressed by pixel size), which is only exemplary, and other picture sizes commonly used in the art can be used as the size protection range of the feature map as long as the subsequent processing is convenient.
S18, linearizing the characteristic diagram, and activating sigmoid to obtain binary output;
s19, training by using the feature map calculated in the step S7 and the binary output calculated in the step S8 and using a cross entropy loss function BCE for binary classification as a loss function, wherein the loss function is as follows:
Loss＝0.5*Lossmap+0.5*Lossbinary… … … … … … … … … formula 1;
in the formula (I), the compound is shown in the specification,
Lossmap＝-(y7x7log(p)+(1-y7x7) log (1-p)) … … … … … … formula 2;
wherein, y7x7The method is a 7x7 feature matrix, wherein the value of 0 represents attack, the value of 1 represents a real living body, and p is the prediction probability;
Lossbianry═ - (ylog (p)) + (1-y) log (1-p)) … … … … … … … formula 3;
wherein y is a number, a value of 0 indicates an attack, a value of 1 indicates a real living body, and p is a predicted probability.
Second embodiment
A human face living body detection method adapting to different light rays is disclosed, and referring to fig. 8, the detection method comprises the following steps:
s21, collecting images by using a binocular camera, and respectively collecting a visible light image and a near infrared light image;
s22, carrying out face detection on the collected visible light image and near infrared light, if the face cannot be detected, continuously carrying out detection, and if the face is detected, entering the next step;
s23, a: calculating the variance of the difference value of three channels by the visible light face image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2, and the variance of subtracting the difference value of the channel 1 from the channel 3;
b: resampling the near-infrared face image to accord with a near-infrared face living body detection model;
s24, a: calculating the variance average value of the visible light face image, comparing the variance average value with a set variance threshold value, if the variance average value is smaller than the variance threshold value, judging that the picture is a black-and-white or gray-scale picture, and if the variance average value is larger than the variance threshold value, judging that the picture is a color picture;
b: importing a resample image of the near-infrared face image into a trained near-infrared face living body detection model for calculation, and judging and outputting whether the near-infrared face image is detected as a living body; specifically, the model is obtained by training according to the training method.
And S25, carrying out logic AND operation on the visible light image judgment result and the near infrared light image judgment result, wherein when the visible light image is judged to be a color photograph and the near infrared image is judged to be a living body, the detection target is a living body, otherwise, the detection target is a non-living body.
Third embodimentExample (b)
A computer readable storage medium having stored thereon computer instructions which when executed perform the aforementioned liveness detection method.
For details, the method is described in the foregoing section, and is not repeated here.
It will be appreciated by those of ordinary skill in the art that all or a portion of the steps of the various methods of the embodiments described above may be performed by associated hardware as instructed by a program that may be stored on a computer readable storage medium, which may include non-transitory and non-transitory, removable and non-removable media, to implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
Computer program code required for the operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visualbasic, Fortran2003, Perl, COBOL2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or processing device. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Fourth embodiment
A human face living body detection system adapting to different light rays is disclosed, referring to fig. 9, the system comprises an optical image acquisition device 1 and a computer 2 which are in telecommunication connection, the optical image acquisition device 1 is a binocular camera comprising a visible light lens and a near infrared light lens so as to acquire a visible light image and a near infrared image simultaneously and transmit the two acquired images to the computer 2, and the computer 2 receives image data of the optical image acquisition device 1 and operates the living body detection method described in the second embodiment to judge whether a detection target is a living body.
The computer 2 comprises a visible light image pre-judging unit, a near-infrared image pre-judging unit, a living body detection comprehensive judging unit and a result output unit, and the method is as follows.
The visible light image pre-judging unit is used for calculating the variance of the difference value of three channels of the received visible light image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2 and the variance of subtracting the difference value of the channel 1 from the channel 3; and comparing the calculated variance with a set variance threshold value to pre-judge whether the picture is a color picture.
And the near-infrared image pre-judging unit receives the near-infrared image, firstly re-samples the near-infrared face image, then introduces the re-sampled image into a trained near-infrared face living body detection model for calculation, and pre-judges whether the near-infrared image is detected as a living body.
And the living body detection comprehensive judgment unit receives the judgment results of the visible light image pre-judgment unit and the near infrared image pre-judgment unit, performs logic and operation and judges whether the detection target is a living body.
And the result output unit receives the judgment result of the living body detection comprehensive judgment unit and visually displays the real-time judgment result.
By the method and the system, the human face living body detection under different light rays such as dark light, backlight, strong light, sunshine and the like is realized, only one near-infrared human face living body detection model needs to be trained, the algorithm speed is improved, and the detection efficiency is improved.
It is to be noted that different embodiments may produce different advantages, and in different embodiments, any one or combination of the above advantages may be produced, or any other advantages may be obtained.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (7)
1. A near-infrared human face living body detection model training method is characterized by comprising the following steps:
s11, collecting a human face living body detection training sample by using a near infrared camera in a binocular camera, wherein the collected sample comprises a real human living body sample and a non-living body sample under different illumination conditions;
s12, the collected samples are sorted, a face detector is used for detecting an original image, the detected face is cut, and the cut face picture is used as a training sample; the method comprises the following steps of intercepting a face photo of stored living body data as a positive sample, and intercepting a face of stored non-living body data as a negative sample;
s13, resampling the sample to obtain a resampling image;
s14, carrying out convolution calculation, batch normalization, ReLU activation and maximum pooling calculation on the resampling images;
s15, inputting the result after the maximum pooling calculation into a dense block for calculation;
s16, inputting the result of the dense block calculation into the transition block for calculation;
s17, carrying out convolution operation of 1x1 on the calculated result of the transition block, and then carrying out sigmoid activation to obtain a feature map;
s18, linearizing the characteristic diagram, and activating sigmoid to obtain binary output;
s19, training by using the feature map calculated in the step S7 and the binary output calculated in the step S8 and using a cross entropy loss function BCE for binary classification as a loss function, wherein the loss function is as follows:
Loss＝0.5*Lossmap+0.5*Lossbinary… … … … … … … … … formula 1;
in the formula (I), the compound is shown in the specification,
Lossmap＝-(y7x7log(p)+(1-y7x7) log (1-p)) … … … … … … formula 2;
wherein, y7x7The method is a 7x7 feature matrix, wherein the value of 0 represents attack, the value of 1 represents a real living body, and p is the prediction probability;
Lossbianry═ - (ylog (p)) + (1-y) log (1-p)) … … … … … … … formula 3;
wherein y is a number, a value of 0 indicates an attack, a value of 1 indicates a real living body, and p is a predicted probability.
2. Training method according to claim 1, characterized in that: the different lighting conditions in step S11 include normal light, dim light, strong light, backlight, and sunlit.
3. Training method according to claim 1, characterized in that: the resampled graph size in step S13 is 112 × 112px, and the feature graph size in step S17 is 7 × 7 px.
4. A human face living body detection method adapting to different light rays is characterized by comprising the following steps:
s21, collecting images by using a binocular camera, and respectively collecting a visible light image and a near infrared light image;
s22, carrying out face detection on the collected visible light image and near infrared light, if the face cannot be detected, continuously carrying out detection, and if the face is detected, entering the next step;
s23, a: calculating the variance of the difference value of three channels by the visible light face image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2, and the variance of subtracting the difference value of the channel 1 from the channel 3;
b: resampling the near-infrared face image to accord with a near-infrared face living body detection model;
s24, a: calculating the variance average value of the visible light face image, comparing the variance average value with a set variance threshold value, if the variance average value is smaller than the variance threshold value, judging that the picture is a black-and-white or gray-scale picture, and if the variance average value is larger than the variance threshold value, judging that the picture is a color picture;
b: importing a resample image of the near-infrared face image into a trained near-infrared face living body detection model for calculation, and judging and outputting whether the near-infrared face image is detected as a living body;
and S25, carrying out logic AND operation on the visible light image judgment result and the near infrared light image judgment result, wherein when the visible light image is judged to be a color photograph and the near infrared image is judged to be a living body, the detection target is a living body, otherwise, the detection target is a non-living body.
5. The in-vivo detection method according to claim 4, characterized in that: the near-infrared light human face in-vivo detection model in step S24b is obtained by training according to the training method of any one of claims 1 to 3.
6. A computer-readable storage medium having stored thereon computer instructions, characterized in that: the computer instructions when executed perform the liveness detection method of claim 4 or 5.
7. The utility model provides a human face live body detecting system who adapts to different light which characterized in that: the system comprises an optical image acquisition device (1) and a computer (2) which are in telecommunication connection, wherein the optical image acquisition device (1) is a binocular camera comprising a visible light lens and a near infrared light lens so as to simultaneously acquire a visible light image and a near infrared image and transmit the two acquired images to the computer (2), and the computer (2) receives image data of the optical image acquisition device (1) and operates the living body detection method of claim 4 or 5 to judge whether a detection target is a living body; the computer (2) comprises:
the visible light image pre-judging unit is used for calculating the variance of the difference value of three channels of the received visible light image, calculating the face intercepted by the visible light image, and respectively calculating the variance of the difference value of subtracting the channel 2 from the channel 1 of the face image, the variance of subtracting the difference value of the channel 3 from the channel 2 and the variance of subtracting the difference value of the channel 1 from the channel 3; comparing the calculated variance with a set variance threshold value, and pre-judging whether the photo is a color photo;
the near-infrared image pre-judging unit receives the near-infrared image, firstly re-samples the near-infrared face image, then introduces the re-sampled image into a trained near-infrared face living body detection model for calculation, and pre-judges whether the near-infrared image is detected as a living body;
the living body detection comprehensive judgment unit receives the judgment results of the visible light image pre-judgment unit and the near infrared image pre-judgment unit, performs logic and operation and judges whether a detection target is a living body;
and the result output unit receives the judgment result of the living body detection comprehensive judgment unit and visually displays the real-time judgment result.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862299A (en) * | 2017-11-28 | 2018-03-30 | 电子科技大学 | A kind of living body faces detection method based on near-infrared Yu visible ray binocular camera |
| CN108764298A (en) * | 2018-04-29 | 2018-11-06 | 天津大学 | Electric power image-context based on single classifier influences recognition methods |
| CN108898112A (en) * | 2018-07-03 | 2018-11-27 | 东北大学 | A kind of near-infrared human face in-vivo detection method and system |
| CN109272048A (en) * | 2018-09-30 | 2019-01-25 | 北京工业大学 | A kind of mode identification method based on depth convolutional neural networks |
| CN109346159A (en) * | 2018-11-13 | 2019-02-15 | 平安科技（深圳）有限公司 | Case image classification method, device, computer equipment and storage medium |
| CN110516576A (en) * | 2019-08-20 | 2019-11-29 | 西安电子科技大学 | Near-infrared living body faces recognition methods based on deep neural network |
| CN111222380A (en) * | 2018-11-27 | 2020-06-02 | 杭州海康威视数字技术股份有限公司 | Living body detection method and device and recognition model training method thereof |
| CN111339369A (en) * | 2020-02-25 | 2020-06-26 | 佛山科学技术学院 | Video retrieval method, system, computer equipment and storage medium based on depth features |
| CN111398291A (en) * | 2020-03-31 | 2020-07-10 | 南通远景电工器材有限公司 | Flat enameled electromagnetic wire surface flaw detection method based on deep learning |
| CN111931594A (en) * | 2020-07-16 | 2020-11-13 | 广州广电卓识智能科技有限公司 | Face recognition living body detection method and device, computer equipment and storage medium |
| CN112183454A (en) * | 2020-10-15 | 2021-01-05 | 北京紫光展锐通信技术有限公司 | Image detection method and device, storage medium and terminal |
| CN112507922A (en) * | 2020-12-16 | 2021-03-16 | 平安银行股份有限公司 | Face living body detection method and device, electronic equipment and storage medium |
| CN112766365A (en) * | 2021-01-18 | 2021-05-07 | 南京多金网络科技有限公司 | Training method of neural network for intelligent shadow bending detection |
-
2021
- 2021-05-14 CN CN202110528863.6A patent/CN113139517B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107862299A (en) * | 2017-11-28 | 2018-03-30 | 电子科技大学 | A kind of living body faces detection method based on near-infrared Yu visible ray binocular camera |
| CN108764298A (en) * | 2018-04-29 | 2018-11-06 | 天津大学 | Electric power image-context based on single classifier influences recognition methods |
| CN108898112A (en) * | 2018-07-03 | 2018-11-27 | 东北大学 | A kind of near-infrared human face in-vivo detection method and system |
| CN109272048A (en) * | 2018-09-30 | 2019-01-25 | 北京工业大学 | A kind of mode identification method based on depth convolutional neural networks |
| CN109346159A (en) * | 2018-11-13 | 2019-02-15 | 平安科技（深圳）有限公司 | Case image classification method, device, computer equipment and storage medium |
| CN111222380A (en) * | 2018-11-27 | 2020-06-02 | 杭州海康威视数字技术股份有限公司 | Living body detection method and device and recognition model training method thereof |
| CN110516576A (en) * | 2019-08-20 | 2019-11-29 | 西安电子科技大学 | Near-infrared living body faces recognition methods based on deep neural network |
| CN111339369A (en) * | 2020-02-25 | 2020-06-26 | 佛山科学技术学院 | Video retrieval method, system, computer equipment and storage medium based on depth features |
| CN111398291A (en) * | 2020-03-31 | 2020-07-10 | 南通远景电工器材有限公司 | Flat enameled electromagnetic wire surface flaw detection method based on deep learning |
| CN111931594A (en) * | 2020-07-16 | 2020-11-13 | 广州广电卓识智能科技有限公司 | Face recognition living body detection method and device, computer equipment and storage medium |
| CN112183454A (en) * | 2020-10-15 | 2021-01-05 | 北京紫光展锐通信技术有限公司 | Image detection method and device, storage medium and terminal |
| CN112507922A (en) * | 2020-12-16 | 2021-03-16 | 平安银行股份有限公司 | Face living body detection method and device, electronic equipment and storage medium |
| CN112766365A (en) * | 2021-01-18 | 2021-05-07 | 南京多金网络科技有限公司 | Training method of neural network for intelligent shadow bending detection |
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|---|---|
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