CN107007257A - The automatic measure grading method and apparatus of the unnatural degree of face - Google Patents
The automatic measure grading method and apparatus of the unnatural degree of face Download PDFInfo
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- CN107007257A CN107007257A CN201710161341.0A CN201710161341A CN107007257A CN 107007257 A CN107007257 A CN 107007257A CN 201710161341 A CN201710161341 A CN 201710161341A CN 107007257 A CN107007257 A CN 107007257A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/0059—Measuring for diagnostic purposes; Identification of persons using light, e.g. diagnosis by transillumination, diascopy, fluorescence
- A61B5/0077—Devices for viewing the surface of the body, e.g. camera, magnifying lens
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7264—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems
- A61B5/7267—Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems involving training the classification device
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/172—Classification, e.g. identification
Abstract
The present invention provides a kind of automatic measure grading method and apparatus of facial unnatural degree, including：The unnatural degree automatic measure grading module of data acquisition module, data preprocessing module, training grader and human face set gradually.The data acquisition module includes human face's static data collecting unit and face Dynamic Data Acquiring unit；The data preprocessing module is by face static data pretreatment unit, face dynamic data pretreatment unit.The beneficial effects of the invention are as follows：Monitoring in real time and assessment human face's situation, and then the real-time and objectively evaluation unnatural degree of human face, and being positioned to facial unnatural position.Subject can provide Medical Authentication material according to the index of objective quantification for postoperative beauty and shaping dispute case, or in daily life, assess to provide for tester's automatic face and objectively facilitate means.Or provide non-physiological parameter for examination of detecting a lie（Expression parameter）Foundation.
Description
Technical field
The invention belongs to field of orthopedic surgery, technical field of image processing, rehabilitation appliances field, psychological field, specifically
It is related to a kind of real time evaluating method and device for assessing the unnatural degree of human face.
Background technology
With the raising of quality of life level, requirement of the people to figure and features also more and more higher, for because of traffic accident, outer
Figure and features is not good caused by the reasons such as wound, tumour and the reason such as beauty, and selection plastic surgery is more next come the people for improving figure and features defect
It is more.Design and method in plastic surgery operations are influenceed by subjective factors such as personal habits, experiences, and effect is without prediction
Property, it is random larger with blindness, while still medical tangle subject occurred frequently.In addition, facial muscle control is abnormal
The unnatural degree of face can be influenceed, influence appearance is attractive in appearance.
At present, to the assessment of the facial naturalness of face, merely by eye-observation, subjectivity is very strong, and only face
It can be just identified by the human eye after unnatural quantitative change to a certain extent, degree not unnatural to face carries out accurate quantification automatic measure grading
Method and apparatus.In apparatus of the present invention, by gathering the static state and dynamic data of the postoperative face of patient, and trained by early stage
Good grader carries out classification automatic measure grading to it.More reflect the unnatural situation of tester's face, to test objective reality
The unnatural degree of person's face carries out quantitative evaluation, and the right-safeguarding that can for example fail for tester's beauty provide corresponding data, with compared with
Strong legal sense；The unnatural degree assessment of face can also be voluntarily carried out for tester and provides simple and reliable in daily life
Square law device.
" a lie detector " (Lie Detector) can be used for assisting investigation in crime survey, to understand the suspicion inquired
The psychologic status of people, so as to judge whether it is related to punishment case." detecting a lie " is not to survey " lie " in itself, but thought-read reason institute is stimulated
The change of caused physiological parameter, such as pulse, breathing and dermatopolyneuritis (referred to as " skin electricity ").Wherein, skin electricity is most sensitive, is to detect a lie
Main basis, at present the existing many cities in the whole nation a lie detector is incorporated into public security, judicial circuit.But, can a lie detector play
Normal effect, the design of external environment condition, testee's individual state, the level and problem of testing teacher with test is all close
It is related.Some tests as condition not enough, it is final gainless.In apparatus of the present invention, by the unnatural degree of face
Rating evaluation, it is more time saving and energy saving to judge subject with the presence or absence of lying suspicion, and do not restrained by condition.
The content of the invention
The present invention is intended to provide a kind of automatic measure grading device of the unnatural degree of face face, for subject provide face self
The method of examination, it is portable and simple to operate, make the unnatural degree of automatic accurate evaluator face, and facial unnatural position is determined
Position is possibly realized.
The present invention provides a kind of real-time apparatus for evaluating of facial unnatural degree, including：The data acquisition module that sets gradually,
Data preprocessing module, training grader and the unnatural degree automatic measure grading module of human face.The data acquisition module includes people
Facial static data collecting unit and face Dynamic Data Acquiring unit；The data preprocessing module is pre- by face static data
Processing unit, face dynamic data pretreatment unit.
Accordingly, the present invention also provides a kind of method of the real-time apparatus for evaluating using facial unnatural degree, including following
Several steps：
Step A：Data acquisition is carried out to the face of subject；；
Step B：The data gathered are pre-processed；
Step C：The study that the unnatural degree of face assesses grader is carried out with the method for machine learning；
Step D：With the grader trained, the facial unnatural degree of subject is graded.
The present invention uses above technical scheme, the advantage is that, one kind can in real time be gathered, monitored and evaluator face automatically
The method and apparatus of the unnatural degree in portion, objectively reflect the unnatural degree of human face.Can accurately assess subject's face face not from
Right situation, and facial unnatural position is positioned, and then provide shaping and beauty postoperative evaluation measures for subject, it is possible
Shaping failure dispute case provide Medical Authentication material；Or in daily life, being provided for the assessment of tester's automatic face can
The means leaned on.Or provide effective foundation for examination of detecting a lie.
It is preferred that, in the step B, the view data to collection strengthens, and denoising is for future use.
It is preferred that, in step C, including following several steps：
Step C1:It is used as sample by a large amount of human face photos, is trained study；
Step C1:The static state and behavioral characteristics of facial unnatural degree are extracted, and then is found corresponding to facial unnatural degree
Strong feature；
Physiological structure based on human face expression, Ekman etc. define it is corresponding quantify rule, i.e., every kind of expression is by which flesh
Meat is produced, and each muscle is how to act on the specific expression of generation, and how each muscle cooperates with the specific expression of generation.We will
These quantify rule and are used as static nature.Described static nature can be included, size, color, profile and shape etc..
For example static nature can extract feature according to space domain model of the face in each video, such as left and right eye
Size, symmetry, looks spacing etc.；Behavioral characteristics extract feature according to changing pattern of the face between multiframe, can include
Speed and the direction of motion.Speed can be obtained by motion estimation algorithms such as optical flow field or Block- matchings.
Light stream is the two-dimentional instantaneous velocity that moving object is observed that pixel is moved on surface, and available computational methods are
Gray differential method, Region Matching method, the method based on energy and the method based on phase.By taking the method based on phase as an example：
Each two field picture in sequence is input to one group of Gabor filter, bandpass filtering pretreatment, Gabor filtering is carried out
The output response of device is R (X, t)=ρ (X, t) ejφ(X,t), wherein X=(x1,x2) it is each pixel position, φ on plane picture
(X t) is output phase.For point X on equiphase contour line, need to meeting φ, (X, t)=c, c is constant.Above formula two ends are simultaneously right
T derivations are obtained
It is the speed of a certain pixel, φX
(φx,φy) it is phase gradient.When phase gradient normalizes the speed V on directionn=α n, whereinFor normalization side
To.Simultaneous is obtained
Or, learn to carry out advanced features extraction automatically using depth network；
Or, the feature that feature and deep learning with reference to obtained by priori are drawn is inputted as training, using label as prison
Superintend and direct, feature and label are handled with convolutional neural networks structure, generate the grader trained.
Wherein, label be can be nature, it is more unnatural, very unnatural, very unnatural be setting.
The present invention further uses above technical scheme, the advantage is that, by taking deep learning as an example, can pass through a large amount of faces
Photo is inputted as sample, can be put into convolutional neural networks, and training study obtains the strong feature based on data.Or according to priori
Knowledge, the principal character point of face face is extracted from active shape model algorithm (Active Shape Model, ASM)：Eye
Angle, eye center, eyebrow, nose, cheekbone, the corners of the mouth, chin profile etc.；Then face subregion is divided, the feature of extraction is utilized
Point determines the position of face organ and region muscle, selects suitable according to the area of each organ size and each region facial muscles
The sample window of pixel size, extracts the sampling block of all subregion；Facial zone membership vector is asked for, by all subregion with putting down
Equal face, which is compared, seeks difference；Multidigit professional is assisted a ruler in governing a country to exercise supervision to the overall merit of facial unnatural degree learning training,
The feature of the unnatural degree of human face is automatically extracted in big data, well-drilled grader-sorter model nerve net is obtained
Network.
It is preferred that, in the step D, it may include following steps：
Step D1：For newly entering pending human face data, using the feature extracting method or depth according to priori
Learn obtained strong aspect indexing, determine feature set.
Step D2：Characteristic set is accessed into grader, the unnatural degree of output face.
The present invention uses above technical scheme, the advantage is that, automatically extracts the standard diagrams of the unnatural degree of human face, right
The static state and dynamic data of pretreated face face, using the method for machine learning, automatically extract sign human face not certainly
The characteristic parameter so spent, while being positioned to facial unnatural position.
It is preferred that, the specific targets in the step D1 are such as：Facial muscle contraction speed, facial muscle movements direction are various
Property, the local asymmetry of human face expression linkage, face or so, local anomaly is twitched, and each index is given by comprehensive analysis
The deviation and transmission function of weight, calculating and machine learning model average face, so as to obtain the result of quantitative evaluation.
It is preferred that, rating scale includes：To pretreated static image data, human face expression parameter is extracted；
The present invention further uses above technical scheme, the advantage is that, pretreated dynamic image data can be carried
Take the contraction process for changing over time facial muscles.It is 0 by the deciding grade and level of human face's sculpture, the comedian that facial expression is enriched is determined
Level is 100, to each Distribution Indexes weight of the assessment of the unnatural degree of human face of extraction, is the classification of people's facial expression naturalness
Scoring.
It is preferred that, extracting human face expression parameter includes：Facial muscle contraction speed can be between sequential frame image motion
Estimation, facial muscle movements direction diversity, human face expression linkage, the left-right asymmetry property of face and to pretreated dynamic
State view data is extractable to change over time at least one of contraction process of facial muscles.
The present invention further uses above technical scheme, the advantage is that, extracting human face expression parameter includes：Facial muscles
Contraction speed can be between sequential frame image estimation, e.g., Block- matching or optical flow approach obtain to calculate；Facial muscles are transported
Dynamic direction diversity, the characteristic point coordinate vector detected is converted to the description of corresponding mimetic muscle movement effects, is used as degree
The input of amount system, via being classified after training classifier training, is calculated with this and obtains measurement results；Human face expression links
Property, the Expression analysis that can be combined based on AU encodes the part of face in Haar feature bases using joint Haar features
Linkage change；The left-right asymmetry property of face, by geometry pretreatment and gray scale pretreatment, sets up normalized human face data, than
Compared with the similarity of left and right face)；To the extractable contraction process for changing over time facial muscles of pretreated dynamic image data
Deng.It is 0 by the deciding grade and level of human face's sculpture, comedian's deciding grade and level that facial expression is enriched is 100, unnatural to the human face of extraction
Each Distribution Indexes weight of the assessment of degree, is people's facial expression naturalness rank scores.
The beneficial effects of the invention are as follows：Monitoring in real time and human face's situation is assessed, and then in real time and objectively appraiser face
The unnatural degree in portion, is compared to existing subjective judgement method, time saving and energy saving, not by artificial deviation effects.And can be to face
Unnatural position is positioned.Subject can provide medical treatment according to the index of objective quantification for postoperative beauty and shaping dispute case
Expert evidence, or in daily life, provided for the assessment of tester's automatic face and objectively facilitate means.Or it is careful to detect a lie
Non- physiological parameter (expression parameter) foundation of offer is provided.
Brief description of the drawings
Fig. 1 face feature point testing results.
The unnatural degree automatic measure grading method and apparatus block diagram of Fig. 2 human faces.
Fig. 3 static data acquisition processing modules.
Fig. 4 Dynamic Data Acquiring processing modules.
The unnatural degree estimation flow figure of Fig. 5 human faces.
Embodiment
Below in conjunction with the accompanying drawings, the preferably embodiment to the present invention is described in further detail：
The present invention assesses the unnatural degree of human face in real time by gathering the static data and dynamic data of human face's situation
Integrated device, for subject provide personalization facial unnatural degree evaluation scheme, be postoperative beauty and shaping dispute case
Medical Authentication material is provided, or in daily life, is assessed for tester's automatic face and reliable means is provided.Or to survey
Lie, which is examined, provides non-physiological parameter (expression parameter) foundation.
The present invention is carried out further using the automatic measure grading of the postoperative facial unnatural degree of human face's cosmetic surgery as embodiment
Describe in detail.Intensity of anomaly when " the unnatural degree of face " in this embodiment refers to reflect human face's contraction of muscle activity,
Such as craniofacial asymmetry, facial stiff degree.This embodiment is directed to use with machine learning and carrys out grouped data, first by expert
Sample data is analyzed and scoring is played, sample and label are then trained into grader as input.Specifically can be by number
The deep learning trained according to being supplied to using the model of multiple graders or by multiple training datas or test data set
Model.In instances, data can be generated to confidence level with the matching degree of grader and associated with the classification of data.
In embodiment, data acquisition system is not limited to the image/video information for needing to classify, and can also include contributes to the Accurate classification mankind
It is difficult the data additional information excavated.In instances, database can be with continuous updating.
The structured flowchart of the integrated device of this in the present invention is as shown in Fig. 2 specific as follows：
Step 1：Data acquisition module
In this embodiment, the module is the data acquisition device based on video camera, contains human face's static data and adopts
Collection and face Dynamic Data Acquiring, as shown in Figure 3.In Dynamic Data Acquiring, 1) make following required movement as requested, such as：
Wail, laugh, anger etc., the video segment of 10 seconds can be cut into according to sequence of movement, each video segment is referred to as a sample；2)
The process of subject's switching expression is gathered, such as turns sad by happiness, happiness is turned by anger.The video segment of 20 seconds can be cut into, is each regarded
Frequency fragment can be used as a sample.
Step 2：Data preprocessing module
In this embodiment, this module is mainly the static state and dynamic number synchronously, independently collected to above-mentioned module
Data preprocess, in order to follow-up processing, as shown in figure 4, the module is pre-processed by face static data, face dynamic data is pre-
Two little module compositions of processing.Wherein, the view data to collection strengthens, and denoising etc. is handled for future use.
Step 3：Train grader
In this embodiment, the module is mainly used as sample by a large amount of human face photos, and with reference to professional person's demarcation
As a result, study is trained, the static state (in indivedual frames) and behavioral characteristics (many interframe) of facial unnatural degree is can extract, and then looked for
To the strong feature corresponding to facial unnatural degree, it also can automatically learn to carry out advanced features extraction using depth network, can also tie
The feature that feature and deep learning obtained by closing priori are drawn is inputted as training, and then obtains well-drilled grader,
The output of wherein process is the set for all data for representing genealogical classification.
Step 4：The unnatural degree automatic measure grading of human face
In this embodiment, the action difference of the data of pretreatment and average face is mainly compared by the module, is come
Judge that the local muscle contraction movement of face is brought unnatural.The data of pretreatment are measured with the grader trained
Change index extraction, specific targets are such as：Facial muscle contraction speed, facial muscle movements direction diversity, human face expression linkage,
Face or so part asymmetry, local anomaly is twitched, and each index gives weight by comprehensive analysis, is calculated and machine learning
The deviation and transmission function of model average face, the result can as the unnatural degree of human face quantitative evaluation.
Rating scale：To pretreated static dynamic image data, extracting human face expression parameter, (e.g., facial muscles are received
Contracting speed can be between sequential frame image estimation, e.g., Block- matching or optical flow approach obtain to calculate；Facial muscle movements
Direction diversity, the characteristic point coordinate vector detected is converted to the description of corresponding mimetic muscle movement effects, is used as measurement
The input of system, via being classified after training classifier training, is calculated with this and obtains measurement results；Human face expression links
Property, the Expression analysis that can be combined based on AU encodes the part of face in Haar feature bases using joint Haar features
Linkage change；The left-right asymmetry property of face, by geometry pretreatment and gray scale pretreatment, sets up normalized human face data, than
Compared with the similarity of left and right face)；To the extractable contraction process for changing over time facial muscles of pretreated dynamic image data
Deng.
It is 0 by the deciding grade and level of human face's sculpture, comedian's deciding grade and level that facial expression is enriched is 100, can use House-
Brackmann scorings are basic as classification, and fraction by each position, tried to achieve by unnatural degree weighting in level, and weight coefficient can be existed by feature
Determined after weight normalization in training network, facial positions, texture, action equal weight are high corresponding to strong feature.Also can be linear
Return different characteristic and choose gained classification results and expert analysis mode, sort R square values to adjust, the high person of R square values such as mouth is weighed
Weight highest.The proportional system increases constantly dynamic with data volume and adjusted.
In the present invention, the flow of the specific implementation of this embodiment is as shown in figure 5, detailed step is as follows：
Step 1：Receive the subject that the unnatural degree of human face is assessed, need to sit quietly in human face data acquisition instrument (as imaged
Machine) before.
Step 2：After all preparations are ready, start data acquisition.
Step 3：Data to collection are pre-processed, the normalization comprising data, remove background illumination differentia influence etc.
Operation.
Step 4：With the method for machine learning, by taking deep learning as an example, sample can be used as by a large amount of human face photos and inputted,
Convolutional neural networks can be put into, study is trained.Human face characteristic point is extracted first, for example, can be calculated from active shape model
Method (Active Shape Model, ASM) extracts the principal character point of face face：Canthus, eye center, eyebrow, nose, cheekbone
Bone, the corners of the mouth, chin profile etc.；Then face subregion is divided, face organ and region muscle are determined using the characteristic point of extraction
Position, according to the area of each organ size and each region facial muscles select appropriate pixels size sample window, extract
The sampling block of all subregion；Facial zone membership vector is asked for, all subregion and average face are compared and seek difference；Assist a ruler in governing a country
Multidigit professional carries out complete supervised learning training to the overall merit of facial unnatural degree, and people is automatically extracted in big data
The feature of the unnatural degree of face, obtains well-drilled grader-sorter model neutral net.
Step 5：With the grader trained, the facial unnatural degree of subject is graded.
Step 6：Selection printing rating result.
Protection point
1. in the present invention, Treatment Analysis is carried out to human face data using the method for machine learning, the unnatural degree of face is realized
Quantitative evaluation, and be accurately positioned, in the protection domain of this patent.
2. based on the embodiment in the present invention, the technical staff in the field that this patent is related to is not making creative labor
The every other embodiment obtained under the premise of dynamic, belongs to the scope of this patent protection.
Above content is to combine specific preferred embodiment further description made for the present invention, it is impossible to assert
The specific implementation of the present invention is confined to these explanations.For general technical staff of the technical field of the invention,
On the premise of not departing from present inventive concept, some simple deduction or replace can also be made, should all be considered as belonging to the present invention's
Protection domain.
Claims (9)
1. a kind of real-time apparatus for evaluating of facial unnatural degree, it is characterised in that including：The data acquisition module that sets gradually,
Data preprocessing module, training grader and the unnatural degree automatic measure grading module of human face.The data acquisition module includes people
Facial static data collecting unit and face Dynamic Data Acquiring unit；The data preprocessing module is pre- by face static data
Processing unit, face dynamic data pretreatment unit.
2. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that bag
Include following steps：
Step A：Data acquisition is carried out to the face of subject；；
Step B：The data gathered are pre-processed；
Step C：The study that the unnatural degree of face assesses grader is carried out with the method for machine learning；
Step D：With the grader trained, the facial unnatural degree of subject is graded.
3. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that institute
State in step B, the view data to collection strengthens, denoising is for future use.
4. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that step
In rapid C, including following several steps：
Step C1:It is used as sample by a large amount of human face photos, is trained study;
Step C1:The static state and behavioral characteristics of facial unnatural degree are extracted, and then finds the strong spy corresponding to facial unnatural degree
Levy；
Or, learn to carry out advanced features extraction automatically using depth network；
Or, the feature that feature and deep learning with reference to obtained by priori are drawn is inputted as training, and then it is good to obtain training
Good grader.
5. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that institute
State in step D, including following steps：
Step D1：The grader trained carries out quantizating index extraction to the data of pretreatment, and by comprehensive analysis each
Index gives the deviation and transmission function of weight, calculating and machine learning model average face;
Step D2：Using result as the unnatural degree of human face quantitative evaluation.
6. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that institute
State the specific targets in step D1：Facial muscle contraction speed, facial muscle movements direction diversity, human face expression linkage,
Face or so part asymmetry, local anomaly is twitched, and each index gives weight by comprehensive analysis, is calculated and machine learning
The deviation and transmission function of model average face, so as to obtain the result of quantitative evaluation.
7. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that comment
Level standard：To pretreated static image data, human face expression parameter is extracted；Pretreated dynamic image data can be carried
Take contraction process for changing over time facial muscles etc..It is 0 by the deciding grade and level of human face's sculpture, the comedian that facial expression is enriched
Define the level as 100, be people's facial expression naturalness point to each Distribution Indexes weight of the assessment of the unnatural degree of human face of extraction
Level scoring.
8. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that carry
Human face expression parameter is taken to include：Facial muscle contraction speed can be between sequential frame image estimation, facial muscle movements
Direction diversity, human face expression linkage, the left-right asymmetry property of face and to pretreated dynamic image data it is extractable with
At least one of contraction process of time change facial muscles.
9. a kind of method of real-time apparatus for evaluating using facial unnatural degree as claimed in claim 1, it is characterised in that from
The dynamic standard diagrams for extracting the unnatural degree of human face：To the static state and dynamic data of pretreated face face, using machine
The method of study, automatically extracts the characteristic parameter for characterizing the unnatural degree of human face, while being positioned to facial unnatural position.
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| CN107633207A (en) * | 2017-08-17 | 2018-01-26 | 平安科技（深圳）有限公司 | AU characteristic recognition methods, device and storage medium |
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| CN108416331A (en) * | 2018-03-30 | 2018-08-17 | 百度在线网络技术（北京）有限公司 | Method, apparatus, storage medium and the terminal device that face symmetrically identifies |
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| CN110516626A (en) * | 2019-08-29 | 2019-11-29 | 上海交通大学 | A kind of Facial symmetry appraisal procedure based on face recognition technology |
| CN110889332A (en) * | 2019-10-30 | 2020-03-17 | 中国科学院自动化研究所南京人工智能芯片创新研究院 | Lie detection method based on micro expression in interview |
| CN111062936A (en) * | 2019-12-27 | 2020-04-24 | 中国科学院上海生命科学研究院 | Quantitative index evaluation method for facial deformation diagnosis and treatment effect |
| CN111914871A (en) * | 2019-05-09 | 2020-11-10 | 李至伟 | Artificial intelligence auxiliary evaluation method and system applied to beauty treatment and electronic device |
| CN111986801A (en) * | 2020-07-14 | 2020-11-24 | 珠海中科先进技术研究院有限公司 | Rehabilitation evaluation method, device and medium based on deep learning |
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