WO2020252917A1 - Procédé et appareil de reconnaissance d'une image floue d'un visage, dispositif terminal et support - Google Patents

Procédé et appareil de reconnaissance d'une image floue d'un visage, dispositif terminal et support Download PDF

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Publication number
WO2020252917A1
WO2020252917A1 PCT/CN2019/103159 CN2019103159W WO2020252917A1 WO 2020252917 A1 WO2020252917 A1 WO 2020252917A1 CN 2019103159 W CN2019103159 W CN 2019103159W WO 2020252917 A1 WO2020252917 A1 WO 2020252917A1
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face
image
blurred
fuzzy
preset
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PCT/CN2019/103159
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English (en)
Chinese (zh)
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徐玲玲
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平安科技（深圳）有限公司
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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
    • 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
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions

Definitions

  • This application belongs to the field of data processing technology, and in particular relates to a fuzzy face image recognition method, device, terminal equipment and medium.
  • the final face image will be blurred.
  • the face image is applied in the next step, it will greatly affect the effect of the actual application. For example, when it is used as the sample data for face recognition and attendance, it will greatly reduce the reliability of face matching, so there is a need for a fuzzy face image that can be identified
  • the method to realize the recognition and screening of fuzzy face images are described in detail below.
  • fuzzy image recognition methods there are some fuzzy image recognition methods in the prior art, but they are all processed and recognized for fuzzy images with lower resolution.
  • these fuzzy image recognition is not designed with the face itself, it makes the fuzzy recognition of the face At times, it is extremely susceptible to the influence of the non-face area in the image, resulting in inaccurate recognition. Therefore, the prior art urgently needs a fuzzy face image recognition method that can be adapted to various types.
  • the embodiments of the present application provide a fuzzy face image recognition method and terminal device to solve the problem that the prior art cannot adapt to various different types of fuzzy face image recognition, so that it is compatible with fuzzy face image recognition.
  • the first aspect of the embodiments of the present application provides a fuzzy face image recognition method, including:
  • the recognition model obtained by training with a variety of fuzzy types of fuzzy face region image samples is used to identify whether the image is a blurred image;
  • the image of the face area to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine the probability that the image to be processed contains a face;
  • the probability that the image to be processed contains a human face is less than a preset face probability threshold, it is determined that the image to be processed is a blurred human face image.
  • a second aspect of the embodiments of the present application provides a fuzzy face image recognition device, including:
  • the face cropping module is used to crop the face contour of the image to be processed and adjust it to a preset image size to obtain the corresponding image of the face area to be processed;
  • the fuzzy image recognition module is used to input the face area image to be processed into a pre-trained fuzzy recognition model for processing, and to identify whether the face area image to be processed is a blurred image, wherein the fuzzy recognition model is a pre-trained fuzzy recognition model.
  • a recognition model obtained by training based on clear face region image samples and multiple fuzzy types of blurred face region image samples is used to identify whether the image is a blurred image;
  • the face recognition module is configured to, if the image of the face area to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine that the image to be processed contains a human face Probability
  • the fuzzy face recognition module is used to determine that the image to be processed is a blurred face image if the probability that the image to be processed contains a human face is less than a preset face probability threshold.
  • a third aspect of the embodiments of the present application provides a terminal device, including a memory and a processor.
  • the memory stores computer-readable instructions that can run on the processor.
  • the processor executes the computer The following steps are implemented when reading instructions:
  • the recognition model obtained by training with a variety of fuzzy types of fuzzy face region image samples is used to identify whether the image is a blurred image;
  • the image of the face area to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine the probability that the image to be processed contains a face;
  • the probability that the image to be processed contains a human face is less than a preset face probability threshold, it is determined that the image to be processed is a blurred human face image.
  • a fourth aspect of the embodiments of the present application provides a computer-readable storage medium that stores computer-readable instructions, wherein the computer-readable instructions are implemented when executed by at least one processor The following steps:
  • the recognition model obtained by training with a variety of fuzzy types of fuzzy face region image samples is used to identify whether the image is a blurred image;
  • the image of the face area to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine the probability that the image to be processed contains a face;
  • the probability that the image to be processed contains a human face is less than a preset face probability threshold, it is determined that the image to be processed is a blurred human face image.
  • the embodiment of the application has the following beneficial effects: on the one hand, the face contour cropping and image size adjustment are performed on the image to be processed in advance, which not only preserves the key facial features, but also makes the judgment of the facial image blur It can focus on the face itself, reducing the interference of the sharpness of the non-face area. On the other hand, it is based on the actual clear face area image sample and the fuzzy face area image sample training constructed by the fuzzy recognition.
  • Model to perform fuzzy recognition on the image of the face area to be processed, realize the compatibility and accurate recognition of multiple types of facial blur, accurately determine whether the face area to be processed is blurred, and at the same time, in order to prevent face contour recognition and fuzzy recognition model recognition
  • the possible interference in the system leads to an increase in the recognition error.
  • the embodiment of the present application further performs a secondary check on whether the image to be processed that is judged to be a blurred image contains a human face, and when the probability of containing a human face is small, That is, when the accuracy of face recognition is low due to various blur factors, the image to be processed is finally determined to be a blurred face image, which greatly improves the final compatibility and accuracy of different types of blurred face image recognition .
  • FIG. 1 is a schematic diagram of the implementation process of the fuzzy face image recognition method provided by Embodiment 1 of the present application;
  • FIG. 2 is a schematic diagram of the implementation process of the fuzzy face image recognition method provided in the second embodiment of the present application
  • FIG. 3 is a schematic diagram of the implementation process of the fuzzy face image recognition method provided by Embodiment 3 of the present application;
  • FIG. 4 is a schematic diagram of the implementation process of the fuzzy face image recognition method provided by the fourth embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a fuzzy face image recognition device provided by Embodiment 5 of the present application.
  • FIG. 6 is a schematic diagram of a terminal device provided in Embodiment 6 of the present application.
  • various different types of blurred face image samples are first collected in advance, and the fuzzy recognition model is trained based on these samples.
  • a fuzzy recognition model that can perform fuzzy image recognition is obtained.
  • pre-processing the image to perform face contour cropping and image size adjustment it not only retains the key facial features, but also makes the facial image blurred.
  • the judgment can be focused on the face itself, reducing the interference of the definition of the non-face area.
  • the fuzzy recognition of the processed face area image based on the trained fuzzy recognition model is realized to realize the recognition of a variety of faces.
  • Compatibility of the blur type is accurately recognized, and the face area to be processed is accurately judged whether the face area to be processed is blurred.
  • the embodiment of the present application further Perform a secondary check on whether the image to be processed of the blurred image contains a face, and only when the probability of containing a face is small, that is, when the accuracy of face recognition is low due to various blur factors, the final judgment is made.
  • the image to be processed is a blurred face image, which greatly improves the compatibility and accuracy of final recognition of different types of blurred face images. The details are as follows:
  • Fig. 1 shows the implementation flow chart of the fuzzy face image recognition method provided in the first embodiment of the present application, and the details are as follows:
  • S101 Perform face contour cropping on an image to be processed and adjust it to a preset image size to obtain a corresponding face region image to be processed.
  • the embodiment of the present application first performs face contour cropping on the image to be processed, that is, recognizes the face area in the image to be processed, and It is cropped into an independent face region image to be processed.
  • face contour cropping on the image to be processed, that is, recognizes the face area in the image to be processed, and It is cropped into an independent face region image to be processed.
  • the embodiment of the present application will uniformly scale the size of the extracted face area and adjust it to the preset size. Set the image size, where the specific image size can be set by the technician according to actual needs, preferably, it can be set to 112 ⁇ 112 pixels.
  • the embodiment of the present application itself needs to recognize a blurred face image
  • the face area contained in the image to be processed is also very likely to be blurred, making it difficult to accurately recognize the face area itself. If the requirements for face recognition are too high, it is very likely that some fuzzy face regions will not be recognized, so that it is impossible to perform face contour cropping on the processed image. Therefore, when performing face contour cropping in the embodiment of this application, Use some face recognition methods with lower accuracy requirements for face recognition search and cropping, including but not limited to face contour positioning algorithms for face contour search, and recognize the image of the area that meets the face contour as the face area Make a ruling, or reduce the threshold of face recognition judgment in some traditional face recognition algorithms, so as to improve the ability to recognize blurred faces.
  • multiple face regions can be obtained during face recognition, such as objects similar to the contour of a human face and other human faces other than the target face that were accidentally captured during shooting.
  • face area recognition when performing face area recognition, only the area meeting the requirements of face recognition and having the most pixels is recognized as the face area, and subsequent cropping is performed.
  • S102 Input the face area image to be processed into a pre-trained fuzzy recognition model for processing, and identify whether the face area image to be processed is a blurred image, where the fuzzy recognition model is based on a clear face area image sample and multiple A recognition model obtained by training a fuzzy type of fuzzy face region image sample is used to identify whether the image is a blurred image.
  • fuzzy recognition models include, but are not limited to, binary classification models and neural network models, which are specifically selected by the technicians according to actual needs, and are not limited here.
  • specific model training methods can also be set by the technicians.
  • the embodiment of the present application inputs the cropped face region image to be processed into the fuzzy recognition model, and the model is used for processing to identify whether the face region to be processed is a blurred image.
  • S103 If the image of the face area to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine the probability that the image to be processed contains a face.
  • the face image is picked out. It is precisely based on the above-mentioned practical application requirements that when the embodiment of the present application recognizes the face area image to be processed as a blurred image, it will not directly determine it as a blurred face image, but will perform a secondary check on it. In order to ensure that the final recognition result can meet the needs of practical applications, specifically: considering that whatever fuzzy interference factors will eventually cause the face area itself to become blurred, the embodiment of this application will determine when the second inspection Perform face recognition for the face region image to be processed in the blurred image to obtain the probability that it contains a face.
  • the probability is less than the preset face probability threshold, it means that the face region image in the face region image to be processed has a higher degree of facial blur . It is difficult to recognize the human face normally, so the embodiment of the present application will directly determine that the human face is blurred, that is, the image to be processed is a blurred human face image.
  • the specific face recognition model used is not limited here, but unlike step S101, it is necessary to perform relatively accurate face search and matching on the image of the face area to be processed to determine the degree of blur. It is necessary to select some high-precision face recognition methods for face recognition, including but not limited to some face recognition models based on deep learning model training, or face recognition models based on facial feature point matching.
  • the specific value of the face probability threshold can be set by the technicians themselves, or obtained by processing with reference to the fourth embodiment of the present application, which is not limited here.
  • various different types of fuzzy face image samples are collected in advance, and the fuzzy recognition model is trained based on these samples, so as to obtain a fuzzy recognition model that can perform fuzzy image recognition.
  • the fuzzy recognition model is trained based on these samples, so as to obtain a fuzzy recognition model that can perform fuzzy image recognition.
  • the fuzzy recognition model is trained based on these samples, so as to obtain a fuzzy recognition model that can perform fuzzy image recognition.
  • the image to be processed for face contour cropping and image size adjustment not only the key facial features are retained, but also the judgment of the facial image blur can be focused on the face itself, reducing the clarity interference of the non-face area
  • the fuzzy recognition of the image of the face area to be processed based on the trained fuzzy recognition model the compatibility and accurate recognition of multiple face blur types are realized, and the face area to be processed is accurately judged whether the face area is blurred.
  • the embodiment of the present application further determines Perform a secondary check on whether the processed image of the blurred image contains a human face, and only when the probability of containing a human face is small, that is, when the degree of facial blur is high and it is difficult to recognize the human face normally, the final The image to be processed is determined to be a blurred face image, thereby greatly improving the compatibility and accuracy of the final recognition of different types of blurred face images.
  • the second embodiment of the present application includes:
  • S201 Obtain a sample of a clear face image and a sample of a blurred face image of multiple blur types.
  • the way to obtain the face image sample can be either crawling from the website or shooting by the technician. It is not limited here, but it should be ensured that the obtained blurred face image sample contains the type of light blur and motion blur. Type and resolution blur type of the fuzzy face image samples to ensure the effectiveness of the subsequently trained model. At the same time, the number of the three types of fuzzy face image samples should be approximately the same or similar.
  • the blurred face image sample includes:
  • S301 Acquire a first preset number of blurred face image samples of a light blur type obtained by image collection of a face under different light conditions.
  • different light intensities can be adjusted and multiple light types can be used to change the collection environment and perform face image collection.
  • S302 Acquire a second preset number of blurred facial image samples of a motion blur type taken by the camera at various relative movement speeds different from the human body.
  • different relative movement speeds can also be adjusted to collect face images, so as to obtain blurred face image samples with different blur degrees corresponding to the motion blur types.
  • S303 Obtain a clear face image sample, and perform pixel blurring processing on the clear face image sample to obtain a third preset number of blurred face image samples of a resolution blur type.
  • the pixel blur processing methods here include but are not limited to, for example, the use of mean blur, median blur, Gaussian blur, bilateral blur and zoom blur to blur clear face image samples to obtain the corresponding resolution blur type of blurred face image samples .
  • the number of the three types of fuzzy face image samples in the embodiment of the present application should be approximately close or the same.
  • the first preset number, the second preset number, and the third The value of the preset quantity can be set by the technician.
  • the embodiment of the present application further includes: crawling a preset website to obtain a fourth preset number of blurred face image samples.
  • the fourth preset number, the first preset number, the second preset number, and the third preset number decrease sequentially.
  • the fuzzy face images Due to the large number of samples required, the difficulty of all shooting/processing and the insufficient randomness of the samples, it is difficult to guarantee the final effect. Therefore, it is necessary to crawl the fuzzy face images that may appear in real life, but in actual situations, it can be crawled.
  • the fuzzy type contained in the obtained fuzzy face data is difficult to control. According to the analysis of the image data that can be crawled on the actual website, it is found that the number of various types of fuzzy face images is: resolution blur type >Motion blur type>Light blur type. Therefore, based on actual crawling, in order to balance the number of three types of blurred face images as much as possible, in the embodiment of this application, the first preset number and the second preset number The quantity and the third preset quantity have changed. The fourth preset quantity> the first preset quantity> the second preset quantity> the third preset quantity needs to be met. The specific quantity needs to be determined by the technician The actual crawling situation is set.
  • S202 Perform face contour cropping on the clear face image sample and the blurred face image sample and adjust them to a preset image size to obtain corresponding clear face area image samples and blurred face area image samples.
  • both clear face image samples and blurred face image samples are performed.
  • Face contour cropping, and in the embodiments of this application, fuzzy face recognition is required instead of fine face recognition or feature extraction. Therefore, too much face area data is not required, so when selecting the size
  • the embodiment of the present application preferably selects a smaller size to increase the speed of model training and the recognition speed of the blurred face image of the image to be processed in the first embodiment of the present application as much as possible on the basis of ensuring fuzzy recognition.
  • the specific face contour cutting method can refer to the related description in the first embodiment of the present application, which will not be repeated here.
  • S203 Train a preset two-class classification model based on the clear face area image sample and the blurred face area image sample, and calculate the cross-entropy loss value of the classification result.
  • the face area image sample includes a clear face area image sample and a blurred face area image sample.
  • the specific two-classification model used is not limited here, including but not limited to, for example, the two-classification model based on logistic regression model and the two-classification model based on machine learning, etc., which can be set by technicians according to actual needs.
  • the accuracy of the inspection will be directly affected by the accuracy of the adopted face recognition model and the adopted face probability
  • the validity of the threshold is affected. If the technician directly sets a fixed face probability threshold for judgment, the accuracy of the inspection is easily affected by the subjectivity of the technician, which makes the accuracy difficult to guarantee. Therefore, in order to improve the inspection Effectiveness, as shown in Figure 4, includes:
  • S401 Perform face recognition on the clear face image sample and the blurred face image sample based on the face recognition model, and obtain the probability that each clear face image sample and the blurred face image sample respectively contain a face.
  • S402 Determine a preset face probability threshold based on the probability that each clear face image sample and the blurred face image sample respectively contain a face.
  • the actual selection of the first embodiment of this application will be used in this application.
  • Face recognition model to identify the actual face image samples obtained, and compare the recognition results with the actual labels of each face image sample, and select the ones that can make the two-classification effect the best.
  • the embodiment of the present application uses 70% as the face probability threshold in the first embodiment of the present application.
  • the face probability threshold is calculated and selected based on the actual face recognition model and face image samples, which can avoid the recognition inaccuracy caused by the recognition accuracy of different face recognition models to this application as much as possible.
  • the embodiments of this application can well determine the appropriate threshold according to the recognition ability of the face recognition model on the actual sample, so that the present application In the embodiment, a pair of clear face recognition capabilities realize adaptive optimization.
  • FIG. 5 shows a structural block diagram of a fuzzy face image recognition device provided in an embodiment of the present application.
  • the fuzzy face image recognition device illustrated in Fig. 5 may be the subject of execution of the fuzzy face image recognition method provided in the first embodiment.
  • the fuzzy face image recognition device includes:
  • the face cropping module 51 is used to crop the face contour of the image to be processed and adjust it to a preset image size to obtain the corresponding face region image to be processed.
  • the fuzzy image recognition module 52 is used to input the face area image to be processed into a pre-trained fuzzy recognition model for processing, and to identify whether the face area image to be processed is a blurred image, where the fuzzy recognition model is A recognition model trained in advance based on clear face region image samples and multiple blur types of blurred face region image samples is used to identify whether the image is a blurred image.
  • the face recognition module 53 is configured to, if the face region image to be processed is a blurred image, perform face recognition on the image to be processed based on a preset face recognition model, and determine that the image to be processed contains a face The probability.
  • the fuzzy face recognition module 54 is configured to determine that the image to be processed is a blurred face image if the probability that the image to be processed contains a human face is less than a preset face probability threshold.
  • the fuzzy face image recognition device further includes:
  • the sample acquisition module is used to acquire clear face image samples and blurred face image samples of various blur types.
  • the sample cropping module is used to crop the human face contour of the clear human face image sample and the blurred human face image sample and adjust it to the preset image size to obtain the corresponding clear human face region image sample and The image sample of the blurred face area.
  • the model training module is configured to train a preset binary classification model based on the clear face area image sample and the blurred face area image sample, and calculate the loss value of the cross entropy of the classification result.
  • the iterative training module is used to update and train the binary classification model based on the gradient descent method if the loss value is greater than the preset loss threshold, until the loss value is less than or equal to the preset loss threshold or the number of iterations reaches the preset Set the frequency threshold, complete the training, and obtain the fuzzy recognition model.
  • sample acquisition module includes:
  • sample acquisition module also includes:
  • model training module includes
  • the loss value of the cross entropy of the classification result is calculated based on the following formula (1):
  • the face area image sample includes a clear face area image sample and a blurred face area image sample.
  • the fuzzy face image recognition device further includes:
  • the size of the sequence number of each step in the foregoing embodiment does not mean the order of execution.
  • the execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.
  • the terms “first”, “second”, etc. are used in the text in some embodiments of the present application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another.
  • the first table may be named the second table, and similarly, the second table may be named the first table without departing from the scope of the various described embodiments.
  • the first form and the second form are both forms, but they are not the same form.
  • Fig. 6 is a schematic diagram of a terminal device provided by an embodiment of the present application.
  • the terminal device 6 of this embodiment includes a processor 60 and a memory 61.
  • the memory 61 stores computer readable instructions 62 that can run on the processor 60.
  • the steps in the foregoing embodiments of the fuzzy face image recognition method are implemented, such as steps 101 to 105 shown in FIG. 1.
  • the processor 60 executes the computer-readable instructions 62
  • the functions of the modules/units in the foregoing device embodiments, such as the functions of the modules 51 to 54 shown in FIG. 5, are realized.
  • the terminal device 6 may be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device may include, but is not limited to, a processor 60 and a memory 61.
  • FIG. 6 is only an example of the terminal device 6 and does not constitute a limitation on the terminal device 6. It may include more or less components than shown in the figure, or a combination of certain components, or different components.
  • the terminal device may also include an input sending device, a network access device, a bus, and the like.
  • the so-called processor 60 may be a central processing unit (Central Processing Unit, CPU), other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Ready-made programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the memory 61 may be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6.
  • the memory 61 may also be an external storage device of the terminal device 6, for example, a plug-in hard disk equipped on the terminal device 6, a smart memory card (Smart Media Card, SMC), or a Secure Digital (SD) Card, Flash Card, etc.
  • the memory 61 may also include both an internal storage unit of the terminal device 6 and an external storage device.
  • the memory 61 is used to store the computer readable instructions and other programs and data required by the terminal device.
  • the memory 61 can also be used to temporarily store data that has been sent or will be sent.
  • the functional units in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit can be implemented in the form of hardware or software functional unit.
  • the integrated module/unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • this application implements all or part of the procedures in the above-mentioned embodiments and methods, and can also be completed by instructing relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium.
  • the computer-readable instruction is executed by the processor, the steps of the foregoing method embodiments can be implemented.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • ROM read only memory
  • PROM programmable ROM
  • EPROM electrically programmable ROM
  • EEPROM electrically erasable programmable ROM
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

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Abstract

L'invention concerne un procédé et un appareil de reconnaissance d'une image floue d'un visage, un dispositif terminal et un support applicables au domaine technique du traitement de données. Le procédé comprend les étapes consistant à : réaliser une découpe du contour d'un visage sur une image devant être traitée, ajuster ladite image à une taille d'image prédéfinie et obtenir une image d'une zone du visage correspondante devant être traitée (S101) ; entrer ladite image de la zone du visage dans un modèle de reconnaissance de flou préalablement appris en vue d'un traitement et reconnaître si ladite image de la zone du visage est une image floue (S102) ; le cas échéant, procéder à une reconnaissance du visage sur ladite image sur la base d'un modèle de reconnaissance de visage prédéfini et déterminer la probabilité que ladite image contienne un visage (S103) ; et, si la probabilité que ladite image contienne un visage est inférieure à un seuil de probabilité de visage prédéfini, déterminer que ladite image est une image de visage floue (S104). Le procédé améliore considérablement la compatibilité et la précision de la reconnaissance finale de différents types d'images de visages floues.
PCT/CN2019/103159 2019-06-20 2019-08-29 Procédé et appareil de reconnaissance d'une image floue d'un visage, dispositif terminal et support WO2020252917A1 (fr)

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Cited By (9)

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CN112766235A (zh) * 2021-02-24 2021-05-07 北京嘀嘀无限科技发展有限公司 人脸识别方法、装置、设备、存储介质和计算机程序产品
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CN117240607A (zh) * 2023-11-10 2023-12-15 北京云尚汇信息技术有限责任公司 一种基于安全计算机的安全认证方法

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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CN112669207A (zh) * 2020-12-21 2021-04-16 四川长虹电器股份有限公司 一种基于电视摄像头的人脸图像分辨率增强方法
CN112965604A (zh) * 2021-03-29 2021-06-15 深圳市优必选科技股份有限公司 手势识别方法、装置、终端设备及计算机可读存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106951825A (zh) * 2017-02-13 2017-07-14 北京飞搜科技有限公司 一种人脸图像质量评估***以及实现方法
CN107133948A (zh) * 2017-05-09 2017-09-05 电子科技大学 基于多任务卷积神经网络的图像模糊与噪声评测方法
EP3333768A1 (fr) * 2016-12-07 2018-06-13 Samsung Electronics Co., Ltd. Procédé et appareil de détection de cible
CN108241821A (zh) * 2016-12-23 2018-07-03 北京三星通信技术研究有限公司 图像处理设备和方法
CN108288027A (zh) * 2017-12-28 2018-07-17 新智数字科技有限公司 一种图像质量的检测方法、装置及设备

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109829396B (zh) * 2019-01-16 2020-11-13 广州杰赛科技股份有限公司 人脸识别运动模糊处理方法、装置、设备及存储介质

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3333768A1 (fr) * 2016-12-07 2018-06-13 Samsung Electronics Co., Ltd. Procédé et appareil de détection de cible
CN108241821A (zh) * 2016-12-23 2018-07-03 北京三星通信技术研究有限公司 图像处理设备和方法
CN106951825A (zh) * 2017-02-13 2017-07-14 北京飞搜科技有限公司 一种人脸图像质量评估***以及实现方法
CN107133948A (zh) * 2017-05-09 2017-09-05 电子科技大学 基于多任务卷积神经网络的图像模糊与噪声评测方法
CN108288027A (zh) * 2017-12-28 2018-07-17 新智数字科技有限公司 一种图像质量的检测方法、装置及设备

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112949423A (zh) * 2021-02-07 2021-06-11 深圳市优必选科技股份有限公司 物体识别方法、物体识别装置及机器人
CN112949423B (zh) * 2021-02-07 2024-05-24 深圳市优必选科技股份有限公司 物体识别方法、物体识别装置及机器人
CN112766235A (zh) * 2021-02-24 2021-05-07 北京嘀嘀无限科技发展有限公司 人脸识别方法、装置、设备、存储介质和计算机程序产品
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CN115171197A (zh) * 2022-09-01 2022-10-11 广州市森锐科技股份有限公司 一种高精度图像信息识别方法、***、设备及存储介质
CN117240607A (zh) * 2023-11-10 2023-12-15 北京云尚汇信息技术有限责任公司 一种基于安全计算机的安全认证方法
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