CN114565962A - Face image processing method and device, electronic equipment and storage medium - Google Patents
Face image processing method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN114565962A CN114565962A CN202210189323.4A CN202210189323A CN114565962A CN 114565962 A CN114565962 A CN 114565962A CN 202210189323 A CN202210189323 A CN 202210189323A CN 114565962 A CN114565962 A CN 114565962A
- Authority
- CN
- China
- Prior art keywords
- face
- image processing
- detection frame
- image
- model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 21
- 238000001514 detection method Methods 0.000 claims abstract description 249
- 238000012545 processing Methods 0.000 claims abstract description 207
- 238000000034 method Methods 0.000 claims abstract description 49
- 238000012216 screening Methods 0.000 claims abstract description 36
- 238000000605 extraction Methods 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 14
- 230000004044 response Effects 0.000 claims description 4
- 230000008569 process Effects 0.000 abstract description 13
- 238000004364 calculation method Methods 0.000 abstract description 9
- 238000010586 diagram Methods 0.000 description 21
- 238000005516 engineering process Methods 0.000 description 12
- 230000006870 function Effects 0.000 description 11
- 238000004891 communication Methods 0.000 description 10
- 238000004422 calculation algorithm Methods 0.000 description 9
- 230000000694 effects Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 238000010295 mobile communication Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 5
- 230000010365 information processing Effects 0.000 description 4
- 238000002372 labelling Methods 0.000 description 4
- 230000005236 sound signal Effects 0.000 description 4
- 238000007726 management method Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000013527 convolutional neural network Methods 0.000 description 2
- 239000000284 extract Substances 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 230000003993 interaction Effects 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 239000003550 marker Substances 0.000 description 2
- 230000001902 propagating effect Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000003190 augmentative effect Effects 0.000 description 1
- 238000013475 authorization Methods 0.000 description 1
- 230000000295 complement effect Effects 0.000 description 1
- 229910052802 copper Inorganic materials 0.000 description 1
- 239000010949 copper Substances 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 238000003384 imaging method Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 230000004807 localization Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 229910044991 metal oxide Inorganic materials 0.000 description 1
- 150000004706 metal oxides Chemical class 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 230000002441 reversible effect Effects 0.000 description 1
- 239000004576 sand Substances 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The disclosure relates to a face image processing method and device, electronic equipment and a storage medium. And inputting the face image to be processed into a face detection model, and outputting at least one face detection frame. And carrying out image processing on the face region in the face detection frame with the size larger than or equal to the detection frame threshold value through at least one image processing model to obtain an image processing result. According to the embodiment of the invention, the detection frame threshold is determined according to the minimum input image size of all models applied in the face image processing process, so that the face region needing to be processed in the face image to be processed is obtained through screening, the redundant calculation generated by identifying the region which does not meet at least one model input requirement is reduced, and the efficiency of the face image processing process is improved.
Description
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a method and an apparatus for processing a face image, an electronic device, and a storage medium.
Background
The human face image processing algorithm plays a key role in related application of computer vision, and the human face image processing algorithms such as human face recognition, human face matching, human face quality or attribute detection and the like are deployed in many visual applications nowadays. In the related art, after a face image to be subjected to image processing is obtained, all faces in the face image are identified, so that unnecessary redundant calculation is generated. The redundant calculation influences the reasoning speed, so that the face image processing algorithm is difficult to be deployed in some devices with poor calculation capacity.
Disclosure of Invention
The disclosure provides a face image processing method and device, electronic equipment and a storage medium.
According to a first aspect of the present disclosure, there is provided a face image processing method, including:
acquiring a face image to be processed;
determining a detection frame threshold according to the minimum input image size corresponding to each image processing model in at least one image processing model;
inputting the face image to be processed into a face detection model, and outputting at least one face detection frame;
screening the at least one face detection frame according to the detection frame threshold value to obtain a target detection frame;
and carrying out image processing on the face area in the target detection frame according to the at least one image processing model to obtain an image processing result.
According to the embodiment of the invention, the detection frame threshold is determined according to the minimum input image size of all models applied in the face image processing process, so that the face region needing to be processed in the face image to be processed is obtained through screening, the redundant calculation generated by identifying the region which does not meet at least one model input requirement is reduced, and the efficiency of the face image processing process is improved.
In one possible implementation, the determining a detection frame threshold according to a minimum input image size corresponding to each of at least one image processing model includes:
determining a minimum input image size corresponding to each of at least one image processing model;
determining a maximum value in each of the minimum input image sizes as a detection frame threshold.
The embodiment of the disclosure selects the maximum value in the minimum input image sizes of all the image processing models as the threshold value of the detection frame, and reduces the possibility that the face image cannot be processed due to the fact that the face image does not meet the size requirement of any image processing model.
In a possible implementation manner, the acquiring a to-be-processed human face image includes:
determining the size information of the face to be processed in the field angle of the image acquisition device in real time;
and responding to the size information which is larger than or equal to the preset acquisition size, and acquiring an image through the image acquisition device to obtain a face image to be processed.
The face image to be processed can be acquired in a snapshot mode, image acquisition is automatically carried out when a user is close to the image acquisition device, and the possibility of image mis-acquisition is reduced through the preset acquisition size.
In one possible implementation, the determining a detection frame threshold according to a minimum input image size corresponding to each of at least one image processing model includes:
determining a minimum input image size corresponding to each of at least one image processing model;
determining a maximum value of the acquisition size and each of the minimum input image sizes as a detection frame threshold.
According to the embodiment of the disclosure, in a snapshot scene, the detection frame threshold is determined according to the acquisition size of the limited snapshot condition and the minimum input image size of different image processing models, so that the possibility of image processing on a human face object which does not need image processing is reduced, that is, the redundant calculation amount is reduced, and the image processing efficiency is improved.
In a possible implementation manner, the image processing model includes a face quality detection model, and the image processing the face region in the target detection frame according to the at least one image processing model to obtain an image processing result includes:
screening the face area in the target detection frame according to the face quality detection model to obtain a target face area;
and processing the target face region image through the non-face quality detection model in the at least one image processing image to obtain an image processing result.
According to the embodiment of the invention, the face region in the target detection frame is screened again by the face quality, so that the possibility of obtaining an inaccurate image processing result due to image processing on the face with poor quality is reduced, and the accuracy of the whole face image processing is further improved.
In a possible implementation manner, the screening the face region in the target detection frame according to the face quality detection model to obtain a target face region includes:
inputting the face area in each target detection frame into a face quality detection model to obtain at least one face quality score, wherein each face quality score has a corresponding score threshold;
and determining the face area with all the face quality scores larger than the corresponding score threshold value as a target face area from the plurality of face quality scores.
The embodiment of the disclosure can determine at least one face quality score of the face area in each target detection frame through the face quality detection model, and determine the face area as the target face area only when all the face quality scores are greater than the corresponding score threshold, thereby reducing the possibility of poor quality of the target face area caused by shielding, illumination and other reasons.
In a possible implementation manner, the image processing model includes at least one of a face attribute detection model, a face feature extraction model, a face feature comparison model, and a face feature recognition model.
The embodiment of the disclosure performs face image processing through multiple image processing models, and can meet application scenarios of multiple image processing.
In a possible implementation manner, the detection frame threshold includes a length threshold and a width threshold, the face detection frame size includes a detection frame length and a detection frame width, and the screening the at least one face detection frame according to the detection frame threshold to obtain the target detection frame includes:
comparing the size of the at least one face detection frame with the size of the detection frame threshold;
and determining the face detection frame as a target detection frame in response to the fact that the length of the face detection frame is larger than the length threshold and the width of the face detection frame is larger than the width threshold.
According to the embodiment of the invention, when any one of the length and the width of the face detection frame does not meet the requirement, the face detection frame is judged not to meet the requirement, and the possibility of low accuracy of an image processing result caused by face image processing on a local face collected in a face image to be recognized is reduced. According to a second aspect of the present disclosure, there is provided a face image processing apparatus including:
the image determining module is used for acquiring a face image to be processed;
the threshold value determining module is used for determining a detection frame threshold value according to the minimum input image size corresponding to each image processing model in at least one image processing model;
the face detection module is used for inputting the face image to be processed into a face detection model and outputting at least one face detection frame;
the detection frame screening module is used for screening the at least one face detection frame according to the detection frame threshold value to obtain a target detection frame;
and the image processing module is used for carrying out image processing on the face area in the target detection frame according to the at least one image processing model to obtain an image processing result.
In one possible implementation, the threshold determination module includes:
a first size determination submodule for determining a minimum input image size corresponding to each of the at least one image processing model;
a first threshold determination sub-module for determining a maximum value of each of the minimum input image sizes as a detection frame threshold.
In one possible implementation, the image determination module includes:
the second size determining submodule is used for determining the size information of the face to be processed in the field angle of the image acquisition device in real time;
and the image acquisition submodule is used for responding to the situation that the size information is larger than or equal to the preset acquisition size, and acquiring the image through the image acquisition device to obtain the face image to be processed.
In one possible implementation, the threshold determination module includes:
a third size determination submodule, configured to determine a minimum input image size corresponding to each of the at least one image processing model;
and the second threshold value determining submodule is used for determining the maximum value of the acquisition size and each minimum input image size as a detection frame threshold value.
In one possible implementation, the image processing model includes a face quality detection model, and the image processing module includes:
the region extraction submodule is used for screening the face region in the target detection frame according to the face quality detection model to obtain a target face region;
and the image processing submodule is used for processing the target face region image through the non-face quality detection model in the at least one image processing image to obtain an image processing result.
In one possible implementation, the region extraction sub-module includes:
the quality score determining unit is used for inputting the face area in each target detection frame into a face quality detection model to obtain at least one face quality score, and each face quality score has a corresponding score threshold value;
and the region screening unit is used for determining the face regions of which all the face quality scores are greater than the corresponding score thresholds from the plurality of face quality scores as target face regions.
In a possible implementation manner, the image processing model includes at least one of a face attribute detection model, a face feature extraction model, a face feature comparison model, and a face feature recognition model.
In a possible implementation manner, the detection frame threshold includes a length threshold and a width threshold, the face detection frame size includes a detection frame length and a detection frame width, and the detection frame screening module includes:
the size comparison submodule is used for comparing the size of the at least one face detection frame with the size of the detection frame threshold;
and the detection frame screening submodule is used for responding to the condition that the length of the face detection frame is greater than the length threshold value and the width of the face detection frame is greater than the width threshold value, and determining that the face detection frame is a target detection frame.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the above-described method.
In the embodiment of the disclosure, the detection frame threshold is determined according to the minimum input image size of all models applied in the process of processing the face image, so as to screen and obtain the face region needing image processing in the face image to be processed, reduce redundant calculation generated by identifying the region which does not meet at least one model input requirement, and improve the efficiency of the process of processing the face image.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. Other features and aspects of the present disclosure will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and, together with the description, serve to explain the principles of the disclosure.
FIG. 1 shows a flow diagram of a method of face image processing according to an embodiment of the present disclosure;
FIG. 2 shows a schematic diagram of a face image processing method according to an embodiment of the present disclosure;
FIG. 3 shows a schematic diagram of another face image processing method according to an embodiment of the present disclosure;
FIG. 4 shows a schematic diagram of a face image processing apparatus according to an embodiment of the present disclosure;
FIG. 5 shows a schematic diagram of an electronic device according to an embodiment of the present disclosure;
fig. 6 shows a schematic diagram of another electronic device according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments, features and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The word "exemplary" is used exclusively herein to mean "serving as an example, embodiment, or illustration. Any embodiment described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
Furthermore, in the following detailed description, numerous specific details are set forth in order to provide a better understanding of the present disclosure. It will be understood by those skilled in the art that the present disclosure may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the subject matter of the present disclosure.
In a possible implementation manner, the face image processing method according to the embodiment of the disclosure may be executed by an electronic device such as a terminal device or a server. The terminal device may be any fixed or mobile terminal such as a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, and a wearable device. The server may be a single server or a server cluster of multiple servers. Any electronic device can realize the face image processing method of the embodiment of the disclosure by calling the computer readable instructions stored in the memory through the processor.
The embodiment of the disclosure can be applied to any face image processing scene, such as an application scene for identifying the face identity to unlock, an application scene for face payment, and the like, or an application scene for judging the age, gender attribute, and the like of a person according to the face feature information in the face image.
Fig. 1 shows a flowchart of a face image processing method according to an embodiment of the present disclosure, and as shown in fig. 1, the face image processing method according to an embodiment of the present disclosure may include the following steps S10-S50.
And step S10, acquiring a face image to be processed.
In a possible implementation manner, the face image to be processed is determined by the electronic device, and includes at least one face, and is used for performing image processing on at least one face. Optionally, the face image to be processed may be obtained by acquiring at least one face through an image acquisition device built in or connected to the electronic device. Or, the face image to be processed, which is obtained by collecting at least one face by the image collecting device, can be sent to the electronic device through other devices. In different application scenes, the human face images to be processed have different functions. For example, when the face image processing method is used for face unlocking, a face image to be processed can be obtained by obtaining a face of a user needing unlocking and is used for identifying user unlocking authority through image processing so as to unlock, namely, the face image processing method is applied to an electronic lock terminal, and an image processing result is used for unlocking the electronic lock. When the face image processing method is used for face payment, the face to be processed can be obtained by obtaining the face of the user needing payment and is used for judging the identity of the user through image processing so as to carry out payment, namely, the face image processing method is applied to a payment terminal, and the image processing result is used for confirming a payment account.
Optionally, the face image to be processed may be acquired according to different acquisition rules. The image acquisition control of the image acquisition device can be triggered by a user to acquire the image, or the image acquisition device monitors the face in the field angle in real time and takes a snapshot when the face meets the requirements. For example, the electronic device may determine size information of a face to be processed in a field angle of the image acquisition device in real time, and perform image acquisition through the image acquisition device to obtain a face image to be processed in response to the size information being greater than or equal to a preset acquisition size. That is to say, the electronic device monitors the face in the field angle in the image acquisition device, and controls the image acquisition device to acquire the face to obtain the face image to be processed under the condition that the face size meets the snapshot condition.
Step S20, determining a detection frame threshold according to a minimum input image size corresponding to each image processing model in at least one image processing model.
In a possible implementation manner, in order to improve the efficiency of face image processing, at least one deep learning model may be included in the flow of face image processing algorithms currently executing the face image processing process. The face image processing algorithm stream of the embodiment of the present disclosure may include a face detection model for detecting a position of a face in a face image to be processed, and at least one other image processing model for processing a face region in the face to be processed. Optionally, the image processing model may include a face quality detection model for evaluating the quality of the face in each face region in the face image to be processed. In addition to the face quality detection model, the image processing model may further include at least one of a face attribute detection model, a face feature extraction model, a face feature comparison model, and a face feature recognition model. The face attribute detection model is used for detecting the face attributes, such as the attributes of face gender, age and the like, in each face area in the face image to be processed. The face feature extraction model is used for extracting the features of the face in each face area in the face image to be processed and carrying out face matching identification based on the face features.
Furthermore, the face detection model and each image processing model are pre-trained models, and each model has a minimum input image size. The minimum input image size characterizes the minimum image size that the corresponding model can handle. Because each face region needs to be processed by all image processing models in the face image processing process, when the size of the face region does not meet the requirements of any image processing model, a corresponding image processing result cannot be obtained. In this case, since the final image processing result cannot be obtained, the image processing performed on the face region by the image processing model partially satisfying the requirement is invalid redundant processing, which affects the image processing efficiency and takes up computational effort. Therefore, the detection frame threshold value can be determined according to the minimum input image size corresponding to each image processing model in at least one image processing model, so that the face regions capable of obtaining the image processing result can be obtained in the face regions of the face image to be processed through screening, and the redundant calculation is reduced.
Alternatively, the minimum input image size corresponding to each image processing model in the at least one image processing model may be determined, and then the maximum value in each minimum input image size may be determined as the detection frame threshold. The image processing model of the embodiment of the present disclosure includes a face quality model, a face attribute detection model, and a face feature extraction model as an example for explanation. When the minimum input image size of the face quality model is 30 × 30 pixels, the minimum input image size of the face attribute detection model is 40 × 40 pixels, and the minimum input image size of the face feature extraction model is 50 × 50 pixels, the maximum value 50 × 50 pixels in the minimum input image size is directly determined as the detection frame threshold.
In one possible implementation, the embodiment of the present disclosure may further determine the detection box threshold based on the application scenario and a minimum input image processing corresponding to each image processing model in the at least one image processing model. For example, in a scene obtained by capturing a face image to be processed, the face that the image processing apparatus needs to recognize is a captured face, and is not a face region serving as a background. Based on the characteristic that the image processing device collects the face image to be processed when the size information of the face to be processed in the field angle is larger than or equal to the collection size, the face to be recognized by the electronic equipment is the face image to be processed with the size larger than the collection size. Thus, the detection box threshold may be determined based on a minimum input image size, and an acquisition size, for each of the at least one image processing model. For example, the maximum value of the minimum input image size and the acquisition size corresponding to each of the at least one image processing model is determined as the detection frame threshold.
Still, the image processing model of the embodiment of the present disclosure includes a face quality model, a face attribute detection model, and a face feature extraction model, and the minimum input image size of the face quality model is 30 × 30 pixels, the minimum input image size of the face attribute detection model is 40 × 40 pixels, and the minimum input image size of the face feature extraction model is 50 × 50 pixels. When the face image to be processed is obtained for a face with a size larger than 60 × 60 pixels in the capture field angle, the maximum 60 × 60 pixels in the minimum input image size and the capture size can be determined as the detection frame threshold.
And step S30, inputting the face image to be processed into a face detection model, and outputting at least one face detection frame.
In a possible implementation manner, the electronic device may obtain the to-be-processed face image by detecting the face detection model obtained through pre-training, that is, the to-be-processed face image is input into the trained face detection model, and at least one face detection frame is output. Each face detection frame comprises a face and is used for representing the position of the face in the face image to be processed. After the electronic device obtains at least one face detection frame of the face image to be processed, the electronic device can further extract the region included in the face image to obtain a face region, so as to perform image processing.
And S40, screening the at least one face detection frame according to the detection frame threshold value to obtain a target detection frame.
In a possible implementation manner, to avoid redundant computation, the electronic device may filter at least one face detection frame of the face image to be processed through the detection frame threshold determined in step S20, so as to obtain at least one satisfactory face detection frame as the target detection frame. Optionally, the detection frame threshold may include a length threshold and a width threshold, and the face detection frame size may include a detection frame length and a detection frame width. The determining process of the target detection frame may be comparing at least one face detection frame size with a detection frame threshold, and determining the face detection frame as the target detection frame in response to the length being greater than the length threshold and the face detection frame width being greater than the width threshold. That is, the electronic device determines a face detection frame having a size larger than the detection frame threshold as a detection frame satisfying the requirement, and determines the face detection frame as a target detection frame. Under the condition that one of the length and the width of the face detection frame does not meet the requirement, the face detection frame is not determined as a target detection frame, and incomplete faces can be preliminarily deleted through size screening.
And step S50, performing image processing on the face region in the target detection frame according to the at least one image processing model to obtain an image processing result.
In a possible implementation manner, the electronic device extracts a face region in the face detection frame that passes the screening according to at least one image processing model, that is, a face region in the target detection frame, and performs image processing to obtain an image processing result. Optionally, the image processing modes are different in different application scenes, and the obtained image processing results are also different. For example, when the embodiment of the present disclosure is used for extracting a face attribute and a face feature in a face image, the image processing model may include a face attribute detection model and a face feature extraction model, after extracting a face region in the target detection frame, the electronic device inputs the face region into the face attribute detection model to obtain the face attribute, inputs the face region into the face feature extraction model to obtain the face feature, and uses the face attribute and the face feature as an image processing result. Further, when the embodiment of the present disclosure is used for identifying a face identity in a face image, the image processing model may include a face attribute detection model and a face feature extraction model, after extracting a face region in the target detection frame, the electronic device inputs the face region into the face attribute detection model to obtain a face attribute, and inputs the face region into the face feature extraction model to obtain a face feature. After the face attribute and the face feature are obtained, a labeling feature set is obtained through screening according to the face attribute, feature matching is conducted according to each labeling feature in the labeling feature set and the face feature, and the matched labeling feature is obtained and serves as an image processing result.
Fig. 2 shows a schematic diagram of a face image processing method according to an embodiment of the present disclosure. As shown in fig. 2, after acquiring the to-be-processed face image 20, the electronic device inputs the to-be-processed face image 20 into the face detection model 21, and outputs a corresponding face detection frame 22. Whether each face detection frame 22 is smaller than the detection frame threshold value 23 is judged, so that when the face detection frame 22 is not smaller than, i.e. greater than or equal to, the detection frame threshold value, the face detection frame 22 is determined as a target detection frame, and a face area included in the target detection frame is determined as a face area to be processed. And carrying out image processing 24 on the face region in the target detection frame to obtain a corresponding image processing result 25. When the face detection frame 22 is smaller than the detection frame threshold, it is determined that the face region included therein is not the face region to be processed, and the image processing 26 is not performed on the face region therein.
Further, in order to further improve the processing efficiency of the face image, the face regions which need to be subjected to image processing are screened according to the sizes, and the face regions which pass through size screening can be subjected to quality screening so as to eliminate the face regions which are difficult to obtain the image processing results. Optionally, the image processing model of the embodiment of the present disclosure further includes a face quality detection model, and the quality screening may be implemented by the face quality detection model. That is to say, the face region in the target detection frame may be screened according to the face quality detection model to obtain the target face region. And processing the target face region image through a non-face quality detection model in at least one image processing image to obtain an image processing result. The face quality detection model is used for detecting face quality scores of input face regions, the face quality scores represent the quality conditions of the corresponding face regions, and the larger the face quality score is, the better the corresponding face quality score is represented. Therefore, the electronic device may input the face region in each target detection frame into the face quality detection model to obtain at least one face quality score, each face quality score has a corresponding score threshold, and determine, as the target face region, the face region in which all the face quality scores are greater than the corresponding score threshold. And further carrying out image processing on each target face area to obtain an image processing result. Optionally, the face quality score may include an occlusion score, an illumination score, an angle score, a comprehensive score, and the like, and the electronic device may determine that the quality of the face region is low when any one of the score values does not satisfy a corresponding score threshold, that is, is less than or equal to the corresponding score threshold, and cannot use the face region as the target face region.
Fig. 3 shows a schematic diagram of another face image processing method according to an embodiment of the present disclosure. As shown in fig. 3, after acquiring the to-be-processed face image 30, the electronic device inputs the to-be-processed face image 30 into the face detection model 31, and outputs the corresponding face detection frame 32. It is determined whether each face detection box 32 is smaller than the detection box threshold 33 to perform the face region filtering from the size dimension. When the face detection frame 32 is smaller than the detection frame threshold, it is determined that the face region in the face detection frame does not pass the screening, and is not the face region to be processed, and the image processing 3A is not performed on the face region in the face detection frame. And when the face detection frame 32 is greater than or equal to the detection frame threshold, determining that the face detection frame passes the screening, namely determining that the face detection frame is a target detection frame, extracting a face region 34 included in the target detection frame, and inputting the face region into a face quality detection model 35 to obtain at least one corresponding face quality score 36. It is determined whether each face quality score for the face region is greater than the corresponding score threshold 37 to perform the screening of the face region from the quality dimension. And determining that the face area is not the face area to be processed under the condition that the corresponding at least one face quality score 36 is not greater than the corresponding score threshold value, namely is less than or equal to the corresponding score threshold value, and not performing the image processing 3A. And under the condition that the corresponding total face quality score 36 is greater than the score threshold value, determining the face area as a target face area to be processed, and performing image processing 38 to obtain an image processing result 39.
Based on the face image processing method, the embodiment of the disclosure can determine the threshold of the detection frame according to the minimum input image size of all models applied in the face image processing process, so as to obtain the face region to be identified in the face image to be processed through size dimension screening, reduce redundant calculation generated by identifying the region which does not meet at least one model input requirement, and improve the efficiency of the image processing process. Meanwhile, the human face regions which are difficult to identify can be further screened and removed through the human face quality screening model in the quality dimension, and the efficiency of the image processing process is improved. Based on the screening of the image processing area needing image processing, the embodiment of the disclosure improves the performance of the algorithm, so that the face image processing algorithm can be deployed on equipment with relatively poor computing power to operate.
It is understood that the above-mentioned method embodiments of the present disclosure can be combined with each other to form a combined embodiment without departing from the logic of the principle, which is limited by the space, and the detailed description of the present disclosure is omitted. Those skilled in the art will appreciate that in the above methods of the specific embodiments, the specific order of execution of the steps should be determined by their function and possibly their inherent logic.
In addition, the present disclosure also provides a face image processing apparatus, an electronic device, a computer-readable storage medium, and a program, which can be used to implement any one of the face image processing methods provided by the present disclosure, and the corresponding technical solutions and descriptions and corresponding descriptions in the methods section are not repeated.
Fig. 4 shows a schematic diagram of a face image processing apparatus according to an embodiment of the present disclosure. As shown in fig. 4, the face image processing apparatus according to the embodiment of the present disclosure may include:
the image determining module 40 is used for acquiring a face image to be processed;
a threshold determining module 41, configured to determine a detection frame threshold according to a minimum input image size corresponding to each image processing model in at least one image processing model;
a face detection module 42, configured to input the to-be-processed face image into a face detection model, and output at least one face detection frame;
a detection frame screening module 43, configured to screen the at least one face detection frame according to the detection frame threshold to obtain a target detection frame;
and the image processing module 44 is configured to perform image processing on the face region in the target detection frame according to the at least one image processing model to obtain an image processing result.
In one possible implementation, the threshold determining module 41 includes:
a first size determination submodule for determining a minimum input image size corresponding to each of the at least one image processing model;
a first threshold determination sub-module for determining a maximum value of each of the minimum input image sizes as a detection frame threshold.
In one possible implementation, the image determining module 40 includes:
the second size determining submodule is used for determining the size information of the face to be processed in the field angle of the image acquisition device in real time;
and the image acquisition submodule is used for responding to the fact that the size information is larger than or equal to a preset acquisition size, and acquiring an image through the image acquisition device to obtain a face image to be processed.
In one possible implementation, the threshold determining module 41 includes:
a third size determining sub-module, configured to determine a minimum input image size corresponding to each image processing model in at least one image processing model;
and the second threshold value determining submodule is used for determining the maximum value of the acquisition size and each minimum input image size as a detection frame threshold value.
In one possible implementation, the image processing model includes a face quality detection model, and the image processing module 44 includes:
the region extraction submodule is used for screening the face region in the target detection frame according to the face quality detection model to obtain a target face region;
and the image processing submodule is used for processing the target face region image through the non-face quality detection model in the at least one image processing image to obtain an image processing result.
In one possible implementation, the region extraction sub-module includes:
the quality score determining unit is used for inputting the face area in each target detection frame into a face quality detection model to obtain at least one face quality score, and each face quality score has a corresponding score threshold value;
and the region screening unit is used for determining the face regions of which all the face quality scores are greater than the corresponding score thresholds from the plurality of face quality scores as target face regions.
In a possible implementation manner, the image processing model includes at least one of a face attribute detection model, a face feature extraction model, a face feature comparison model, and a face feature recognition model.
In a possible implementation manner, the detection frame threshold includes a length threshold and a width threshold, the face detection frame size includes a detection frame length and a detection frame width, and the detection frame filtering module 43 includes:
the size comparison submodule is used for comparing the size of the at least one face detection frame with the size of the detection frame threshold;
and the detection frame screening submodule is used for responding to the condition that the length of the face detection frame is greater than the length threshold value and the width of the face detection frame is greater than the width threshold value, and determining that the face detection frame is a target detection frame.
The method has specific technical relevance with the internal structure of the computer system, and can solve the technical problem of how to improve the hardware operation efficiency or the execution effect (including reducing data storage capacity, reducing data transmission capacity, improving hardware processing speed and the like), thereby obtaining the technical effect of improving the internal performance of the computer system according with the natural law.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and specific implementation thereof may refer to the description of the above method embodiments, and for brevity, will not be described again here.
Embodiments of the present disclosure also provide a computer-readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the above-mentioned method. The computer readable storage medium may be a volatile or non-volatile computer readable storage medium.
An embodiment of the present disclosure further provides an electronic device, including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the above-described method.
The disclosed embodiments also provide a computer program product comprising computer readable code or a non-transitory computer readable storage medium carrying computer readable code, which when run in a processor of an electronic device, the processor in the electronic device performs the above method.
The electronic device may be provided as a terminal, server, or other form of device.
Fig. 5 shows a schematic diagram of an electronic device 800 according to an embodiment of the disclosure. For example, the electronic device 800 may be a User Equipment (UE), a mobile device, a User terminal, a cellular phone, a cordless phone, a Personal Digital Assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, or other terminal device.
Referring to fig. 5, electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.
The processing component 802 generally controls overall operation of the electronic device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing components 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
The memory 804 is configured to store various types of data to support operations at the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The power supply component 806 provides power to the various components of the electronic device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the electronic device 800.
The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front facing camera and/or a rear facing camera. The front camera and/or the rear camera may receive external multimedia data when the electronic device 800 is in an operation mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the electronic device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.
The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor assembly 814 includes one or more sensors for providing various aspects of state assessment for the electronic device 800. For example, the sensor assembly 814 may detect an open/closed state of the electronic device 800, the relative positioning of components, such as a display and keypad of the electronic device 800, the sensor assembly 814 may also detect a change in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, orientation or acceleration/deceleration of the electronic device 800, and a change in the temperature of the electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object in the absence of any physical contact. The sensor assembly 814 may also include a light sensor, such as a Complementary Metal Oxide Semiconductor (CMOS) or Charge Coupled Device (CCD) image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 may access a wireless network based on a communication standard, such as a wireless network (Wi-Fi), a second generation mobile communication technology (2G), a third generation mobile communication technology (3G), a fourth generation mobile communication technology (4G), a long term evolution of universal mobile communication technology (LTE), a fifth generation mobile communication technology (5G), or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the electronic device 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium, such as the memory 804, is also provided that includes computer program instructions executable by the processor 820 of the electronic device 800 to perform the above-described methods.
The disclosure relates to the field of augmented reality, and aims to detect or identify relevant features, states and attributes of a target object by means of various visual correlation algorithms by acquiring image information of the target object in a real environment, so as to obtain an AR effect combining virtual and reality matched with specific applications. For example, the target object may relate to a face, a limb, a gesture, an action, etc. associated with a human body, or a marker, a marker associated with an object, or a sand table, a display area, a display item, etc. associated with a venue or a place. The vision-related algorithms may involve visual localization, SLAM, three-dimensional reconstruction, image registration, background segmentation, key point extraction and tracking of objects, pose or depth detection of objects, and the like. The specific application can not only relate to interactive scenes such as navigation, explanation, reconstruction, virtual effect superposition display and the like related to real scenes or articles, but also relate to special effect treatment related to people, such as interactive scenes such as makeup beautification, limb beautification, special effect display, virtual model display and the like. The detection or identification processing of the relevant characteristics, states and attributes of the target object can be realized through the convolutional neural network. The convolutional neural network is a network model obtained by performing model training based on a deep learning framework.
Fig. 6 shows a schematic diagram of another electronic device 1900 according to an embodiment of the disclosure. For example, the electronic device 1900 may be provided as a server or terminal device. Referring to fig. 6, electronic device 1900 includes a processing component 1922 further including one or more processors and memory resources, represented by memory 1932, for storing instructions, e.g., applications, that are executable by processing component 1922. The application programs stored in memory 1932 may include one or more modules that each correspond to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the above-described method.
In an exemplary embodiment, a non-transitory computer readable storage medium, such as the memory 1932, is also provided that includes computer program instructions executable by the processing component 1922 of the electronic device 1900 to perform the above-described methods.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
Computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions 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 server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including 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 using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The computer program product may be embodied in hardware, software or a combination thereof. In an alternative embodiment, the computer program product is embodied in a computer storage medium, and in another alternative embodiment, the computer program product is embodied in a Software product, such as a Software Development Kit (SDK), or the like.
The foregoing description of the various embodiments is intended to highlight various differences between the embodiments, and the same or similar parts may be referred to each other, and for brevity, will not be described again herein.
It will be understood by those skilled in the art that in the method of the present invention, the order of writing the steps does not imply a strict order of execution and any limitations on the implementation, and the specific order of execution of the steps should be determined by their function and possible inherent logic.
If the technical scheme of the application relates to personal information, a product applying the technical scheme of the application clearly informs personal information processing rules before processing the personal information, and obtains personal independent consent. If the technical scheme of the application relates to sensitive personal information, a product applying the technical scheme of the application obtains individual consent before processing the sensitive personal information, and simultaneously meets the requirement of 'express consent'. For example, at a personal information collection device such as a camera, a clear and significant identifier is set to inform that the personal information collection range is entered, the personal information is collected, and if the person voluntarily enters the collection range, the person is regarded as agreeing to collect the personal information; or on the device for processing the personal information, under the condition of informing the personal information processing rule by using obvious identification/information, obtaining personal authorization by modes of popping window information or asking a person to upload personal information of the person by himself, and the like; the personal information processing rule may include information such as a personal information processor, a personal information processing purpose, a processing method, and a type of personal information to be processed.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (11)
1. A face image processing method, characterized in that the method comprises:
acquiring a face image to be processed;
determining a detection frame threshold according to the minimum input image size corresponding to each image processing model in at least one image processing model;
inputting the face image to be processed into a face detection model, and outputting at least one face detection frame;
screening the at least one face detection frame according to the detection frame threshold value to obtain a target detection frame;
and carrying out image processing on the face area in the target detection frame according to the at least one image processing model to obtain an image processing result.
2. The method of claim 1, wherein determining a detection box threshold based on a minimum input image size corresponding to each of the at least one image processing model comprises:
determining a minimum input image size corresponding to each of at least one image processing model;
determining a maximum value in each of the minimum input image sizes as a detection frame threshold.
3. The method according to claim 1, wherein the acquiring the face image to be processed comprises:
determining the size information of the face to be processed in the field angle of the image acquisition device in real time;
and responding to the size information which is larger than or equal to the preset acquisition size, and acquiring an image through the image acquisition device to obtain a face image to be processed.
4. The method of claim 3, wherein determining the detection box threshold according to the minimum input image size corresponding to each of the at least one image processing model comprises:
determining a minimum input image size corresponding to each of at least one image processing model;
determining a maximum value of the acquisition size and each of the minimum input image sizes as a detection frame threshold.
5. The method according to any one of claims 1 to 4, wherein the image processing model comprises a face quality detection model, and the image processing the face region in the target detection frame according to the at least one image processing model to obtain an image processing result comprises:
screening the face area in the target detection frame according to the face quality detection model to obtain a target face area;
and processing the target face region image through the non-face quality detection model in the at least one image processing image to obtain an image processing result.
6. The method of claim 5, wherein the screening the face region in the target detection frame according to the face quality detection model to obtain a target face region comprises:
inputting the face area in each target detection frame into a face quality detection model to obtain at least one face quality score, wherein each face quality score has a corresponding score threshold;
and determining the face areas with all the face quality scores larger than the corresponding score threshold values as target face areas.
7. The method according to any one of claims 1 to 6, wherein the image processing model comprises at least one of a face property detection model, a face feature extraction model, a face feature comparison model and a face feature recognition model.
8. The method according to any one of claims 1 to 7, wherein the detection frame threshold includes a length threshold and a width threshold, the face detection frame size includes a detection frame length and a detection frame width, and the screening the at least one face detection frame according to the detection frame threshold to obtain the target detection frame includes:
comparing the size of the at least one face detection frame with the size of the detection frame threshold;
and determining the face detection frame as a target detection frame in response to the fact that the length of the face detection frame is larger than the length threshold and the width of the face detection frame is larger than the width threshold.
9. A face image processing apparatus, characterized in that the apparatus comprises:
the image determining module is used for acquiring a face image to be processed;
the threshold value determining module is used for determining a detection frame threshold value according to the minimum input image size corresponding to each image processing model in at least one image processing model;
the face detection module is used for inputting the face image to be processed into a face detection model and outputting at least one face detection frame;
the detection frame screening module is used for screening the at least one face detection frame according to the detection frame threshold value to obtain a target detection frame;
and the image processing module is used for carrying out image processing on the face area in the target detection frame according to the at least one image processing model to obtain an image processing result.
10. An electronic device, comprising:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to invoke the memory-stored instructions to perform the method of any one of claims 1 to 8.
11. A computer readable storage medium having stored thereon computer program instructions, which when executed by a processor, implement the method of any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210189323.4A CN114565962A (en) | 2022-02-28 | 2022-02-28 | Face image processing method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210189323.4A CN114565962A (en) | 2022-02-28 | 2022-02-28 | Face image processing method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114565962A true CN114565962A (en) | 2022-05-31 |
Family
ID=81714987
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210189323.4A Withdrawn CN114565962A (en) | 2022-02-28 | 2022-02-28 | Face image processing method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114565962A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117437505A (en) * | 2023-12-18 | 2024-01-23 | 杭州任性智能科技有限公司 | Training data set generation method and system based on video |
-
2022
- 2022-02-28 CN CN202210189323.4A patent/CN114565962A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117437505A (en) * | 2023-12-18 | 2024-01-23 | 杭州任性智能科技有限公司 | Training data set generation method and system based on video |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109740516B (en) | User identification method and device, electronic equipment and storage medium | |
| CN108197586B (en) | Face recognition method and device | |
| CN110674719B (en) | Target object matching method and device, electronic equipment and storage medium | |
| WO2021031609A1 (en) | Living body detection method and device, electronic apparatus and storage medium | |
| US11288531B2 (en) | Image processing method and apparatus, electronic device, and storage medium | |
| CN110569777B (en) | Image processing method and device, electronic device and storage medium | |
| CN111553864B (en) | Image restoration method and device, electronic equipment and storage medium | |
| CN110287671B (en) | Verification method and device, electronic equipment and storage medium | |
| CN109934275B (en) | Image processing method and device, electronic equipment and storage medium | |
| CN112270288A (en) | Living body identification method, access control device control method, living body identification device, access control device and electronic device | |
| CN109034106B (en) | Face data cleaning method and device | |
| CN111435422B (en) | Action recognition method, control method and device, electronic equipment and storage medium | |
| CN110909203A (en) | Video analysis method and device, electronic equipment and storage medium | |
| CN108171222B (en) | Real-time video classification method and device based on multi-stream neural network | |
| CN114332503A (en) | Object re-identification method and device, electronic equipment and storage medium | |
| CN111523346A (en) | Image recognition method and device, electronic equipment and storage medium | |
| CN112184787A (en) | Image registration method and device, electronic equipment and storage medium | |
| CN107977636B (en) | Face detection method and device, terminal and storage medium | |
| CN114187498A (en) | Occlusion detection method and device, electronic equipment and storage medium | |
| CN114445753A (en) | Face tracking recognition method and device, electronic equipment and storage medium | |
| CN114581525A (en) | Attitude determination method and apparatus, electronic device, and storage medium | |
| CN113822798B (en) | Method and device for training generation countermeasure network, electronic equipment and storage medium | |
| CN110781842A (en) | Image processing method and device, electronic equipment and storage medium | |
| CN114565962A (en) | Face image processing method and device, electronic equipment and storage medium | |
| CN109271863B (en) | Face living body detection method and device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20220531 |