WO2022227191A1 - Procédé et appareil de détection de corps vivant inactif, dispositif électronique et support d'enregistrement - Google Patents

Procédé et appareil de détection de corps vivant inactif, dispositif électronique et support d'enregistrement Download PDF

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Publication number
WO2022227191A1
WO2022227191A1 PCT/CN2021/097078 CN2021097078W WO2022227191A1 WO 2022227191 A1 WO2022227191 A1 WO 2022227191A1 CN 2021097078 W CN2021097078 W CN 2021097078W WO 2022227191 A1 WO2022227191 A1 WO 2022227191A1
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reference point
point set
preset
loss value
living body
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PCT/CN2021/097078
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English (en)
Chinese (zh)
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刘杰
庄伯金
曾凡涛
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平安科技（深圳）有限公司
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Publication of WO2022227191A1 publication Critical patent/WO2022227191A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/49—Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection
    • G06V40/45—Detection of the body part being alive
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Definitions

  • the present application relates to the field of biometric identification, and in particular, to a non-active living body detection method, device, electronic device, and computer-readable storage medium.
  • the method for detecting inactive living bodies in background images is usually based on single-frame or multi-frame image texture and analysis of multi-frame eye, mouth, head gesture movements, etc.
  • the stability of the multi-frame image texture method relies heavily on the quality of image acquisition and the completeness of training data.
  • image quality of network calls is poor, especially in bad lighting scenarios. Therefore, this type of application scenario greatly reduces the stability of existing image silent live detection methods based on single-frame or multi-frame textures.
  • a non-active live detection method provided by this application includes:
  • the present application also provides a non-active liveness detection device, the device comprising:
  • the target image acquisition module is used for acquiring original video screenshots, performing reference point selection on the original video screenshots to obtain an initial reference point set; performing position disturbance processing on the initial reference point set to obtain a target reference point set; according to the The initial reference point set and the target reference point set generate a geometric transformation matrix, and the original video screenshot is subjected to geometric transformation processing by using the geometric transformation matrix to obtain a target image;
  • a reference point analysis module for inputting the original video screenshot, the target image and the initial reference point set into a preset reference point analysis network to obtain a prediction reference point set;
  • the loss value calculation module is used to calculate the root mean square loss value between the prediction reference point in the prediction reference point set and the preset real reference point by using the preset root mean square error loss function, and calculate the Perform arithmetic operation on the square root loss value and the preset classification loss value of the two-class network to obtain the final detection loss value;
  • a model training module configured to perform iterative optimization processing on the non-active living detection model constructed by the reference point analysis network and the two-classification network according to the final detection loss value, to obtain a trained non-active living detection model
  • the image detection module is used for acquiring images to be recognized with a preset number of frames, and inputting the images to be recognized into the trained non-active living body detection model to obtain a non-active living body detection result.
  • the present application also provides an electronic device, the electronic device comprising:
  • the present application also provides a computer-readable storage medium, where at least one instruction is stored in the computer-readable storage medium, and the at least one instruction is executed by a processor in an electronic device to implement the non-active living body detection method described below :
  • FIG. 1 is a schematic flowchart of a non-active living body detection method provided by an embodiment of the present application
  • FIG. 2 is a functional block diagram of a non-active living body detection device provided by an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of an electronic device for implementing the non-active living body detection method according to an embodiment of the present application.
  • the embodiments of the present application provide a non-active live detection method.
  • the execution subject of the non-active living body detection method includes, but is not limited to, at least one of electronic devices that can be configured to execute the method provided by the embodiments of the present application, such as a server and a terminal.
  • the non-active liveness detection method may be executed by software or hardware installed in a terminal device or a server device, and the software may be a blockchain platform.
  • the server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
  • the non-active liveness detection method includes:
  • the original video screenshot is an image captured during an online video call, and the image may include a human face and a human face background.
  • the image may include a human face and a human face background.
  • obtaining the original video screenshot, performing reference point selection on the original video screenshot, and obtaining an initial reference point set including:
  • Multiple reference points in the original video screenshot are randomly selected on the two-dimensional rectangular coordinate system to generate an initial reference point set.
  • the number of randomly selected reference points is four.
  • performing position disturbance processing on the initial reference point set to obtain the target reference point set includes:
  • the target coordinate point set is mapped to a two-dimensional rectangular coordinate system to obtain a target reference point set.
  • the disturbance function is a Hash function.
  • geometric transformation refers to an anti-projection from a set with a geometric structure to itself or other such sets
  • the geometric transformation includes image affine transformation and homography transformation.
  • the image affine transformation is to perform a linear transformation on a vector space followed by a translation in geometry to transform it into another vector space
  • the homography transformation is to map the image on the world coordinate system to the pixel coordinate system.
  • geometric transformation matrix to perform geometric transformation processing on the original video screenshot, that is, using the geometric transformation matrix and the position of each pixel in the original video screenshot to perform multiplication processing to obtain a geometrically transformed image. pixel points and generate a target image according to the pixel points.
  • the homography transformation is adopted in this solution, that is, the generated geometric transformation matrix is a homography matrix, and the geometric transformation processing is a homography transformation processing.
  • the generating a geometric transformation matrix according to the initial reference point set and the target reference point set includes:
  • the first arbitrary coordinate and the second arbitrary coordinate of the coordinates of the initial reference point and the coordinates of the target reference point are calculated according to the geometric transformation formula to obtain the geometric transformation matrix.
  • first arbitrary coordinate and the second arbitrary coordinate of the coordinates of the initial reference point and the coordinates of the target reference point are calculated according to the geometric transformation formula to obtain a geometric transformation matrix, including:
  • the first arbitrary coordinate and the second arbitrary coordinate of the coordinates of the initial reference point and the coordinates of the target reference point are calculated according to the geometric transformation formula to obtain a geometric transformation matrix, including:
  • H is the geometric transformation matrix
  • b is the second arbitrary coordinate
  • a is the first arbitrary coordinate
  • T is a fixed parameter.
  • the original video screenshot, the target image and the initial reference point set are input into a preset reference point analysis network, and the reference point analysis network is used to predict the original video screenshot.
  • the position of the initial reference point set on the target image, and obtain the prediction reference point set, the coordinates of the prediction reference point included in the prediction reference point set are the initial reference point set on the original video screenshot on the target image coordinate of.
  • the reference point analysis network may be a deep learning model.
  • the calculation of the root mean square loss value between the prediction reference point in the prediction reference point set and the preset real reference point by using the preset root mean square error loss function includes:
  • MSE is the root mean square error loss value
  • y i is the prediction reference point
  • n is the total number of prediction reference points in the prediction reference point set
  • i refers to the ith prediction reference point.
  • the arithmetic calculation of the root mean square loss value and the preset classification loss value of the two-class network includes calculating the root mean square loss value and the classification loss value of the two-class network. and to obtain the final detection loss value, wherein the classification loss value of the binary classification network can be calculated by using the cross-entropy loss function.
  • the trained non-active living body detection model is used to detect whether the human face in the picture containing the human face belongs to the non-active living body attack.
  • the non-active living body detection model constructed by the reference point analysis network and the two-classification network is iteratively optimized according to the final detection loss value to obtain a trained non-active living body detection model.
  • models including:
  • the non-active living body detection model is a trained non-active living body detection model
  • the internal parameters of the inactive living body detection model may be the gradient of the model or the internal parameters of the model.
  • the preset number of frames may be at least two consecutive or discontinuous images.
  • the to-be-recognized images of the preset number of frames may be multiple consecutive screenshots containing human faces in the chat video; or the images to be recognized of the preset number of frames may be intercepted during identity verification through a camera
  • the obtained multiple screenshots contain human faces, and the multiple screenshots can be 2D images.
  • the non-active living body detection model by inputting the images to be identified with a preset number of frames into the non-active living body detection model, it can be monitored whether the to-be-identified images contain non-active living bodies.
  • This embodiment of the present application obtains a target image by performing geometric transformation processing on the set of initial reference points selected in the original video screenshot and the target reference point obtained through position disturbance processing, and according to the original video screenshot, the target image and the initial reference point
  • the reference point analysis network is trained, and the trained reference point analysis network pays more attention to learning the information related to the geometric transformation of the face, which can reduce the dependence on a large amount of training data, so it can improve the stability of the non-active living body detection, and this application implements
  • a binary classification network is added, which improves the accuracy and generalization, and improves the accuracy of non-active liveness detection. Therefore, the non-active living detection method proposed in this application can solve the problem of low stability of the non-active living detection.
  • FIG. 2 it is a functional block diagram of an inactive living body detection device provided by an embodiment of the present application.
  • the non-active living body detection apparatus 100 described in this application may be installed in an electronic device.
  • the inactive living body detection apparatus 100 may include a target image acquisition module 101 , a reference point analysis module 102 , a loss value calculation module 103 , a model training module 104 and an image detection module 105 .
  • the modules described in this application may also be referred to as units, which refer to a series of computer program segments that can be executed by the processor of the electronic device and can perform fixed functions, and are stored in the memory of the electronic device.
  • each module/unit is as follows:
  • the target image acquisition module 101 is configured to acquire original video screenshots, select reference points for the original video screenshots, and obtain an initial reference point set; perform position disturbance processing on the initial reference point set to obtain a target reference point set; Generate a geometric transformation matrix according to the initial reference point set and the target reference point set, and use the geometric transformation matrix to perform geometric transformation processing on the original video screenshot to obtain a target image;
  • the reference point analysis module 102 is configured to input the original video screenshot, the target image and the initial reference point set into a preset reference point analysis network to obtain a prediction reference point set;
  • the loss value calculation module 103 is configured to use a preset root mean square error loss function to calculate the root mean square loss value between the prediction reference point in the prediction reference point set and the preset real reference point, and calculate the root mean square loss value between the prediction reference point and the preset real reference point.
  • the root mean square loss value and the preset classification loss value of the two-class network are subjected to an arithmetic operation to obtain the final detection loss value;
  • the model training module 104 is configured to iteratively optimize the non-active living body detection model constructed by the reference point analysis network and the two-classification network according to the final detection loss value, and obtain a trained non-active living body detection model;
  • the image detection module 105 is configured to acquire images to be recognized with a preset number of frames, input the images to be recognized into the trained non-active living detection model, and obtain a non-active living detection result.
  • a non-active body detection method including the following steps can be implemented:
  • Step 1 Obtain a screenshot of the original video, select a reference point for the original video screenshot, and obtain an initial set of reference points.
  • the original video screenshot is a picture image captured during an online video call, and the image may include a human face and a human face background.
  • the image may include a human face and a human face background.
  • obtaining the original video screenshot, performing reference point selection on the original video screenshot, and obtaining an initial reference point set including:
  • Multiple reference points in the original video screenshot are randomly selected on the two-dimensional rectangular coordinate system to generate an initial reference point set.
  • the number of randomly selected reference points is four.
  • Step 2 Perform position disturbance processing on the initial reference point set to obtain a target reference point set.
  • performing position disturbance processing on the initial reference point set to obtain the target reference point set includes:
  • the target coordinate point set is mapped to a two-dimensional rectangular coordinate system to obtain a target reference point set.
  • the disturbance function is a Hash function.
  • Step 3 Generate a geometric transformation matrix according to the initial reference point set and the target reference point set, and use the geometric transformation matrix to perform geometric transformation processing on the original video screenshot to obtain a target image.
  • geometric transformation refers to an anti-projection from a set with a geometric structure to itself or other such sets
  • the geometric transformation includes image affine transformation and homography transformation.
  • the image affine transformation is to perform a linear transformation on a vector space followed by a translation in geometry to transform it into another vector space
  • the homography transformation is to map the image on the world coordinate system to the pixel coordinate system.
  • geometric transformation matrix to perform geometric transformation processing on the original video screenshot, that is, using the geometric transformation matrix and the position of each pixel in the original video screenshot to perform multiplication processing to obtain a geometrically transformed image. pixel points and generate a target image according to the pixel points.
  • the homography transformation is adopted in this solution, that is, the generated geometric transformation matrix is a homography matrix, and the geometric transformation processing is a homography transformation processing.
  • the generating a geometric transformation matrix according to the initial reference point set and the target reference point set includes:
  • the first arbitrary coordinate and the second arbitrary coordinate of the coordinates of the initial reference point and the coordinates of the target reference point are calculated according to the geometric transformation formula to obtain the geometric transformation matrix.
  • first arbitrary coordinate and the second arbitrary coordinate of the coordinates of the initial reference point and the coordinates of the target reference point are calculated according to the geometric transformation formula to obtain a geometric transformation matrix, including:
  • H is the geometric transformation matrix
  • b is the second arbitrary coordinate
  • a is the first arbitrary coordinate
  • T is a fixed parameter.
  • Step 4 Input the original video screenshot, the target image and the initial reference point set into a preset reference point analysis network to obtain a prediction reference point set.
  • the original video screenshot, the target image and the initial reference point set are input into a preset reference point analysis network, and the reference point analysis network is used to predict the original video screenshot.
  • the position of the initial reference point set on the target image, and obtain the prediction reference point set, the coordinates of the prediction reference point included in the prediction reference point set are the initial reference point set on the original video screenshot on the target image coordinate of.
  • the reference point analysis network may be a deep learning model.
  • Step 5 Calculate the root mean square loss value between the prediction reference point in the prediction reference point set and the preset real reference point by using the preset root mean square error loss function, and calculate the root mean square loss value. Perform arithmetic operation with the classification loss value of the preset binary classification network to obtain the final detection loss value.
  • the calculation of the root mean square loss value between the prediction reference point in the prediction reference point set and the preset real reference point by using the preset root mean square error loss function includes:
  • MSE is the root mean square error loss value
  • y i is the prediction reference point
  • n is the total number of prediction reference points in the prediction reference point set
  • i refers to the ith prediction reference point.
  • the arithmetic calculation of the root mean square loss value and the preset classification loss value of the two-class network includes calculating the root mean square loss value and the classification loss value of the two-class network. and to obtain the final detection loss value, wherein the classification loss value of the binary classification network can be calculated by using the cross-entropy loss function.
  • Step 6 Perform iterative optimization processing on the non-active living body detection model constructed by the reference point analysis network and the two-classification network according to the final detection loss value to obtain a trained non-active living body detection model.
  • the trained non-active living body detection model is used to detect whether the human face in the picture containing the human face belongs to the non-active living body attack.
  • the non-active living body detection model constructed by the reference point analysis network and the two-classification network is iteratively optimized according to the final detection loss value to obtain a trained non-active living body detection model.
  • models including:
  • the non-active living body detection model is a trained non-active living body detection model
  • the internal parameters of the inactive living body detection model may be the gradient of the model or the internal parameters of the model.
  • Step 7 Acquire images to be recognized with a preset number of frames, input the images to be recognized into the trained non-active living body detection model, and obtain a non-active living body detection result.
  • the preset number of frames may be at least two consecutive or discontinuous images.
  • the to-be-recognized images of the preset number of frames may be multiple consecutive screenshots containing human faces in the chat video; or the images to be recognized of the preset number of frames may be intercepted during identity verification through a camera
  • the obtained multiple screenshots contain human faces, and the multiple screenshots can be 2D images.
  • the non-active living body detection model by inputting the images to be identified with a preset number of frames into the non-active living body detection model, it can be monitored whether the to-be-identified images contain non-active living bodies.
  • This embodiment of the present application obtains a target image by performing geometric transformation processing on the set of initial reference points selected in the original video screenshot and the target reference point obtained through position disturbance processing, and according to the original video screenshot, the target image and the initial reference point
  • the reference point analysis network is trained, and the trained reference point analysis network pays more attention to learning the information related to the geometric transformation of the face, which can reduce the dependence on a large amount of training data, so it can improve the stability of the non-active living body detection, and this application implements
  • a binary classification network is added, which improves the accuracy and generalization, and improves the accuracy of non-active liveness detection. Therefore, the non-active living body detection device proposed in the present application can solve the problem that the stability of the non-active living body detection is not high.
  • FIG. 3 it is a schematic structural diagram of an electronic device for implementing an inactive living body detection method provided by an embodiment of the present application.
  • the electronic device 1 may include a processor 10, a memory 11 and a bus, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an inactive living body detection program 12.
  • the memory 11 includes at least one type of readable storage medium, and the readable storage medium may be volatile or non-volatile.
  • the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (eg, SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like.
  • the memory 11 may be an internal storage unit of the electronic device 1 in some embodiments, such as a mobile hard disk of the electronic device 1 . In other embodiments, the memory 11 may also be an external storage device of the electronic device 1, such as a pluggable mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital) equipped on the electronic device 1.
  • the memory 11 may also include both an internal storage unit of the electronic device 1 and an external storage device.
  • the memory 11 can not only be used to store application software installed in the electronic device 1 and various types of data, such as codes of the non-active living body detection program 12, etc., but also can be used to temporarily store data that has been output or will be output.
  • the processor 10 may be composed of integrated circuits, for example, may be composed of a single packaged integrated circuit, or may be composed of multiple integrated circuits packaged with the same function or different functions, including one or more integrated circuits.
  • Central Processing Unit CPU
  • microprocessor digital processing chip
  • graphics processor and combination of various control chips, etc.
  • the processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and by running or executing programs or modules (such as non- Active living body detection program, etc.), and call data stored in the memory 11 to execute various functions of the electronic device 1 and process data.
  • the bus may be a peripheral component interconnect (PCI for short) bus or an extended industry standard architecture (Extended industry standard architecture, EISA for short) bus or the like.
  • PCI peripheral component interconnect
  • EISA Extended industry standard architecture
  • the bus can be divided into address bus, data bus, control bus and so on.
  • the bus is configured to implement connection communication between the memory 11 and at least one processor 10 and the like.
  • FIG. 3 only shows an electronic device with components. Those skilled in the art can understand that the structure shown in FIG. 3 does not constitute a limitation on the electronic device 1, and may include fewer or more components than those shown in the figure. components, or a combination of certain components, or a different arrangement of components.
  • the electronic device 1 may also include a power supply (such as a battery) for powering the various components, preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that the power management
  • the device implements functions such as charge management, discharge management, and power consumption management.
  • the power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and any other components.
  • the electronic device 1 may further include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.
  • the electronic device 1 may also include a network interface, optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.
  • a network interface optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is usually used in the electronic device 1 Establish a communication connection with other electronic devices.
  • the electronic device 1 may further include a user interface, and the user interface may be a display (Display), an input unit (eg, a keyboard (Keyboard)), optionally, the user interface may also be a standard wired interface or a wireless interface.
  • the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch panel, and the like.
  • the display may also be appropriately called a display screen or a display unit, which is used for displaying information processed in the electronic device 1 and for displaying a visualized user interface.
  • the non-active living body detection program 12 stored in the memory 11 in the electronic device 1 is a combination of multiple instructions, and when running in the processor 10, can realize:
  • the modules/units integrated in the electronic device 1 may be stored in a computer-readable storage medium.
  • the computer-readable storage medium may be volatile or non-volatile.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a removable hard disk, a magnetic disk, an optical disc, a computer memory, a read-only memory (ROM, Read-Only). Memory).
  • the present application also provides a computer-readable storage medium, where the readable storage medium stores a computer program, and when executed by a processor of an electronic device, the computer program can realize:
  • modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
  • each functional module in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.
  • the blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

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Abstract

Procédé et appareil de détection de corps vivant inactif, dispositif électronique et support d'enregistrement lisible par ordinateur, se rapportant à la technologie d'identification biologique et à la technologie de chaîne de blocs. Le procédé consiste à : obtenir un ensemble de points de référence initial dans une capture d'écran vidéo d'origine, et effectuer une perturbation de position pour obtenir un ensemble de points de référence cible ; en fonction de l'ensemble de points de référence initial et de l'ensemble de points de référence cible, réaliser une transformation géométrique sur la capture d'écran vidéo d'origine pour obtenir une image cible ; entrer la capture d'écran vidéo d'origine, l'image cible et l'ensemble de points de référence initial dans un réseau d'analyse de points de référence de façon à obtenir un ensemble de points de référence prédit ; et en fonction d'une valeur de perte de détection finale obtenue, optimiser un modèle de détection de corps vivant inactif pour obtenir un modèle de détection de corps vivant inactif entraîné, et identifier une image à identifier, de façon à obtenir un résultat de détection de corps vivant inactif. L'image cible décrite peut être stockée dans un nœud d'une chaîne de blocs. Le procédé peut résoudre le problème selon lequel la stabilité de la détection de corps vivant inactif n'est pas élevée.
PCT/CN2021/097078 2021-04-28 2021-05-30 Procédé et appareil de détection de corps vivant inactif, dispositif électronique et support d'enregistrement WO2022227191A1 (fr)

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117579804A (zh) * 2023-11-17 2024-02-20 广东筠诚建筑科技有限公司 一种基于ar的装配式建筑构件预布局体验方法及装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110674759A (zh) * 2019-09-26 2020-01-10 深圳市捷顺科技实业股份有限公司 一种基于深度图的单目人脸活体检测方法、装置及设备
US20200019760A1 (en) * 2018-07-16 2020-01-16 Alibaba Group Holding Limited Three-dimensional living-body face detection method, face authentication recognition method, and apparatuses
CN111368731A (zh) * 2020-03-04 2020-07-03 上海东普信息科技有限公司 静默活体检测方法、装置、设备及存储介质
CN111860055A (zh) * 2019-04-29 2020-10-30 北京眼神智能科技有限公司 人脸静默活体检测方法、装置、可读存储介质及设备
CN112215298A (zh) * 2020-10-21 2021-01-12 平安国际智慧城市科技股份有限公司 模型训练方法、装置、设备及可读存储介质

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9129190B1 (en) * 2013-12-04 2015-09-08 Google Inc. Identifying objects in images
CN110163033B (zh) * 2018-02-13 2022-04-22 京东方科技集团股份有限公司 正样本获取方法、行人检测模型生成方法和行人检测方法

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200019760A1 (en) * 2018-07-16 2020-01-16 Alibaba Group Holding Limited Three-dimensional living-body face detection method, face authentication recognition method, and apparatuses
CN111860055A (zh) * 2019-04-29 2020-10-30 北京眼神智能科技有限公司 人脸静默活体检测方法、装置、可读存储介质及设备
CN110674759A (zh) * 2019-09-26 2020-01-10 深圳市捷顺科技实业股份有限公司 一种基于深度图的单目人脸活体检测方法、装置及设备
CN111368731A (zh) * 2020-03-04 2020-07-03 上海东普信息科技有限公司 静默活体检测方法、装置、设备及存储介质
CN112215298A (zh) * 2020-10-21 2021-01-12 平安国际智慧城市科技股份有限公司 模型训练方法、装置、设备及可读存储介质

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117579804A (zh) * 2023-11-17 2024-02-20 广东筠诚建筑科技有限公司 一种基于ar的装配式建筑构件预布局体验方法及装置
CN117579804B (zh) * 2023-11-17 2024-05-14 广东筠诚建筑科技有限公司 一种基于ar的装配式建筑构件预布局体验方法及装置

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