CN109498039A - Personality assessment's method and device - Google Patents
Personality assessment's method and device Download PDFInfo
- Publication number
- CN109498039A CN109498039A CN201811590157.9A CN201811590157A CN109498039A CN 109498039 A CN109498039 A CN 109498039A CN 201811590157 A CN201811590157 A CN 201811590157A CN 109498039 A CN109498039 A CN 109498039A
- Authority
- CN
- China
- Prior art keywords
- personality
- built video
- video
- measurement person
- face
- 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.)
- Pending
Links
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/167—Personality evaluation
-
- 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
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
Landscapes
- Health & Medical Sciences (AREA)
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Psychiatry (AREA)
- Psychology (AREA)
- Artificial Intelligence (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Physics & Mathematics (AREA)
- Social Psychology (AREA)
- Veterinary Medicine (AREA)
- Pathology (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Hospice & Palliative Care (AREA)
- Molecular Biology (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Child & Adolescent Psychology (AREA)
- Developmental Disabilities (AREA)
- Biophysics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Educational Technology (AREA)
- Evolutionary Computation (AREA)
- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physiology (AREA)
- Signal Processing (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Image Analysis (AREA)
Abstract
The present invention provides a kind of personality assessment's method and device.The embodiment of the present invention estimates neural network model by capturing facial exercises feature and personality of the personality measurement person in the Pre-built Video that there is Different Cognitive to stimulate angle for viewing, the personality of personality measurement person is estimated, compared to the mode of questionnaire survey, effectively shorten the estimated time, it improves and estimates efficiency, and it can guarantee the accuracy estimated, while can estimate to obtain the personality estimation results for the fining for meeting Psychology for Scientific Research.
Description
Technical field
The present invention relates to analyze and estimate field, and in particular to a kind of personality assessment's method and device.
Background technique
The theory of peronality in psychology is mainly used for studying " people can be divided into several classes ", " what feature every class people has " asks
Topic.Difference of the different individuals in personality, mainly due to Different Individual when facing similar environmental stimulus information, per each and every one
Body is different to the process of cognitive information, to produce different behavior reactions, that is, shows as different personalities.It is above-mentioned not
Each individual is formed as in the stimulus threshold of sensory perception, attentional resources, working memory, stimulus information characterization side with personality
Formula, semantic memory system, Resolving probiems strategy etc. have differences, and cause different to the process of cognitive information.Again
Further, above-mentioned each species diversity during cognitive information processing, mainly since the brain structure of Different Individual has differences
And caused by the activity of brain network level level of the Different Individual when starting Cognitive task has differences.
According to statement above it is found that the formation of personality is extremely complex, the influence of various factors will receive, therefore be difficult reality
Now to the accurate assessment of the personality of Different Individual.Currently, the assessment to personality: method is generally realized by following several method
One, the mode of questionnaire survey realizes personality assessment, obtains answer of the individual to personality assessment's questionnaire, based on acquisition answer assessment
The personality of body.Although this method can obtain the accurate personality assessment of comparison as a result, still taking a long time, consumption 1 to 2 is generally required
A hour time, inefficiency.Method two, in conjunction with the text evaluation Different Individual in questionnaire survey and social network-i i-platform
Personality, although this method is relatively high in public acceptance level, the accuracy of assessment is poor.Method three, by difference
The assessment to the personality of Different Individual is realized in the analysis of the voice of individual.This method lacks Psychology for Scientific Research support, and obtains
Personality assessment it is relatively simple, such as personality is simply only divided into " lion type " and " tiger type ".
To sum up, current personality assessment's method all cannot achieve quick, the accurate evaluation of personality.
Summary of the invention
(1) the technical issues of solving
In view of the deficiencies of the prior art, the present invention provides a kind of personality assessment's method and device, the prior art is solved
The defect of low efficiency existing for middle personality assessment, inaccuracy.
(2) technical solution
In order to achieve the above object, the present invention is achieved by the following technical programs:
In a first aspect, providing a kind of personality assessment's method, comprising:
The Pre-built Video with Different Cognitive stimulation angle is played to personality measurement person；
Facial exercises feature of personality measurement person during watching every kind of Pre-built Video is obtained respectively；
Neural network model is estimated using personality, facial exercises spy is corresponded in every kind of Pre-built Video of viewing to personality measurement person
Sign is handled, and the personality estimation results of the personality measurement person are obtained；Wherein, it is benefit that the personality, which estimates neural network model,
It is living with face of the personality characteristics and multiple sample measures persons of multiple sample measures persons during watching every kind of Pre-built Video
What dynamic feature training obtained.
In a kind of possible embodiment, the facial exercises feature is predetermined at least one of personality measurement person face
Facial exercises feature in region.
In a kind of possible embodiment, the facial exercises feature is included at least one of the following:
Facial temperature change of the personality measurement person when watching every kind of Pre-built Video；Personality measurement person is prefabricated in every kind of viewing
Changes in heart rate when video；Respiratory variations of the personality measurement person when watching every kind of Pre-built Video.
In a kind of possible embodiment, the face for obtaining personality measurement person during watching every kind of Pre-built Video
Portion's active characteristics, comprising:
Face-image of the personality measurement person when watching every kind of Pre-built Video is obtained, the corresponding K of every kind of Pre-built Video is obtained
Open face-image；Wherein, K is positive integer；
For the corresponding every face-image of every kind of Pre-built Video, it is based on face-image ratio corresponding with the face-image
Compared with the variation of the gray value of image, the corresponding temperature change value of the face-image, heart rate change value and respiratory variations value are determined；
For every kind of Pre-built Video, temperature change value, heart rate based on the corresponding every face-image of the Pre-built Video become
Change value and respiratory variations value determine facial exercises feature of personality measurement person during watching the Pre-built Video.
In a kind of possible embodiment, the corresponding movement images of the described face-image are to shoot the described face
The face-image that the previous second of image or latter second beats are taken the photograph.
In a kind of possible embodiment, the step of neural network model is estimated the method also includes constructing the personality
It is rapid:
Obtain the personality characteristics of multiple sample measures persons；
Obtain facial exercises feature of multiple sample measures persons during watching every kind of Pre-built Video；
Personality characteristics and multiple sample measures persons based on the multiple sample measures person are watching every kind of Pre-built Video
Facial exercises feature in the process, training obtain the personality and estimate neural network model.
In a kind of possible embodiment, the method is estimated questionnaire to personality based on multiple sample measures persons and is answered
Case obtains the personality characteristics of the multiple sample measures person；The personality characteristics includes at least one of the following: that ofNeuroticism obtains
Divide, extropism score, open score, agreeableness score, doing one's duty property score.
In a kind of possible embodiment, it includes 3 convolution & that the personality, which estimates neural network model,
The Softmax layer of MaxPooling, 1 full linking layer and a p=0.5.
In a kind of possible embodiment, it is described with Different Cognitive stimulation angle Pre-built Video include it is following at least
One Pre-built Video:
For the Pre-built Video of sensory perception；For the Pre-built Video of working memory；For the Pre-built Video of semantic memory；Needle
To the Pre-built Video of higher cognitive processing；Wherein the higher cognitive includes reasoning from logic, calculating and problem solving.
In a first aspect, providing a kind of personality assessment's device, comprising:
Video display module, for playing the Pre-built Video with Different Cognitive stimulation angle to personality measurement person；
Feature obtains module, for obtaining facial exercises of personality measurement person during watching every kind of Pre-built Video respectively
Feature；
Personality prediction module, it is prefabricated in every kind of viewing to personality measurement person for estimating neural network model using personality
The corresponding facial active characteristics of video are handled, and the personality estimation results of the personality measurement person are obtained；Wherein, the personality is pre-
It is prefabricated in every kind of viewing using the personality characteristics and multiple sample measures persons of multiple sample measures persons for estimating neural network model
What the facial exercises feature training during video obtained.
(3) beneficial effect
The embodiment of the invention provides a kind of personality assessment's method and devices.Have it is following the utility model has the advantages that
The embodiment of the present invention plays the Pre-built Video with Different Cognitive stimulation angle to personality measurement person first；Later,
Facial exercises feature of personality measurement person during watching every kind of Pre-built Video is obtained respectively；Finally, estimating mind using personality
Through network model, personality measurement person is handled in the corresponding facial active characteristics of every kind of Pre-built Video of viewing, obtains personality survey
The personality estimation results for the person of determining.Wherein, personality estimate neural network model be using multiple sample measures persons personality characteristics with
And facial exercises feature training of multiple sample measures persons watch every kind of Pre-built Video during obtains.Above-mentioned technical proposal
By capturing facial exercises feature and people of the personality measurement person in the Pre-built Video that there is Different Cognitive to stimulate angle for viewing
Lattice estimate neural network model, estimate to the personality of personality measurement person, compared to the mode of questionnaire survey, effectively shorten
Estimated time improves and estimates efficiency, and can guarantee the accuracy estimated, while can estimate to obtain and meet scientific psychology
The personality estimation results of fining.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention for those of ordinary skill in the art without creative efforts, can be with
It obtains other drawings based on these drawings.
Fig. 1 schematically illustrates the flow chart of personality assessment's method of one embodiment of the invention；
Fig. 2 schematically illustrates building personality in personality assessment's method of another embodiment of the present invention and estimates neural network
The flow chart of model；
Fig. 3 schematically illustrates the block diagram of personality assessment's device of one embodiment of the invention.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described embodiment is
A part of the embodiment of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art
Every other embodiment obtained without creative efforts, shall fall within the protection scope of the present invention.
The defect that low efficiency, accuracy are poor, fining degree is low is estimated for personality in the prior art, the application's is some
Embodiment provides a kind of personality predictor method, and this method can not only improve the efficiency that personality is estimated, and can be improved pre-
Estimate accuracy and fining degree.Specifically, as described in Figure 1, the personality predictor method of the present embodiment includes the following steps:
S110, the Pre-built Video with Different Cognitive stimulation angle is played to personality measurement person.
Here, Pre-built Video is the audio-visual-materials worked out by using special preparation method, for exciting personality measurement person
Sensory perception, working memory, semantic memory, higher cognitive processing etc. cognitive behaviors.Specifically, there is Different Cognitive to stimulate angle
Pre-built Video include at least one following Pre-built Video: for the Pre-built Video of sensory perception；For the prefabricated view of working memory
Frequently；For the Pre-built Video of semantic memory；For the Pre-built Video of higher cognitive processing；Wherein the higher cognitive includes logic
Reasoning, calculating and problem solving.This step excites the cognition of personality measurement person living using Pre-built Video as cognitive stimulation source
It is dynamic.
S120, facial exercises feature of personality measurement person during watching every kind of Pre-built Video is obtained respectively.
Here, when personality measurement person watches stimulation video, the face using gun type camera record personality measurement person is living
It is dynamic, record X frame image per second.To the facial exercises video sampling K under the corresponding cognitive activities of each Pre-built Video faces
Image.Wherein, K is positive integer.
The facial exercises feature is the facial exercises feature at least one presumptive area of personality measurement person face.This
In the preset interest region of presumptive area, these scheduled interest regions are that the face of people best embodies the mood of people
Position.The quantity in scheduled interest region can flexibly be set according to actual demand, such as 9 scheduled region of interest of setting
Domain.
Multiple activity points (Landmarks) can be set in each presumptive area, each activity point is by one group of coordinate
Value description:
Dn (Xn, Yn)
D indicates a certain activity point, and n is the serial number of point, and Xn is the abscissa value of n-th of activity point, n-th of Yn
The ordinate value of activity point.
In the specific implementation, 18 to 20 points can be set in each presumptive area.
Here, facial exercises feature includes at least one of the following: face of the personality measurement person when watching every kind of Pre-built Video
Portion's temperature change；Changes in heart rate of the personality measurement person when watching every kind of Pre-built Video；Personality measurement person is prefabricated in every kind of viewing
Respiratory variations when video.
This step is after obtaining for the corresponding face-image of every kind of Pre-built Video, based on face-image and the face-image
The variation of the gray value of corresponding movement images determines the corresponding temperature change value of the face-image, heart rate change value and exhales
Inhale changing value；It is directed to every kind of Pre-built Video, temperature change value, the heart based on the corresponding every face-image of the Pre-built Video later
Rate changing value and respiratory variations value determine facial exercises feature of personality measurement person during watching the Pre-built Video.
The corresponding movement images of the above-mentioned face-image are in the previous second for shooting the face-image or latter second beats
The face-image taken the photograph.Certainly, movement images can also be the image that N second beats before or after shooting face-image is taken the photograph.
Facial changing value, the changing value of heart rate and the changing value of breathing for obtaining temperature shows the video image of face
In color change, above movement images and face-image be located on the adjacent position of video image.
It, can be by the temperature change value of face-image, the variation of heart rate by image enhancement technique in specific embodiment
Value or the description of the changing value of breathing are as follows:
Δ C=(C (n+1)-Cn)
Above-mentioned, C indicates that the color of face-image, C (n+1) indicate the color-values of the face-image of (n+1) second, and Cn is indicated
The color-values of n-th second face-image.
Above-mentioned face-image can be the grayscale image of 32*32.
S130, neural network model is estimated using personality, to personality measurement person in the corresponding face of every kind of Pre-built Video of viewing
Active characteristics are handled, and the personality estimation results of the personality measurement person are obtained；Wherein, the personality estimates neural network mould
Type is personality characteristics and multiple sample measures persons using multiple sample measures persons during watching every kind of Pre-built Video
The training of facial exercises feature obtains.
Above-mentioned personality, which estimates neural network model, can be convolutional neural networks model, including 3 convolution &
The Softmax layer of MaxPooling, 1 full linking layer and a p=0.5.It is different according to each layer neuron number, and divide
Are as follows:
CNN-64:[32,32,64,64]
CNN-96:[48,48,96,200]
CNN-128:[64,64,128,300]
Other than Softmax layers, remaining each layer activation primitive is equal are as follows:
ReLU (x)=max (0, x)
Weight W initialization uses the zero-mean of Krizhevsky, constant standard deviation (Standard Deviation, STD)
Scheme, each layer STD are as follows:
[0.0001,0.001,0.001,0.01,0.1]
Each section of Pre-built Video is made of K face-images, then:
X=(K1, K2......Kn), n are the quantity of Pre-built Video.
The personality estimation results that above-mentioned steps obtain include at least one of the following: ofNeuroticism score, extropism score, open
Putting property score, agreeableness score, doing one's duty property score.
New personality measurement person watches Pre-built Video, and after the completion of viewing, the face-image based on acquisition utilizes the application's
The above method only needs that each dimension scores of five-factor model personality can be calculated within 2-5 seconds, automatically generates five-factor model personality appraisal result, effectively
The efficiency of Personality evaluation is improved, while reducing in Personality evaluation and the variation of method caused by effect, participant is praised as society
It answered without oneself, report any problem, it is only necessary to watch video.
It should be noted that the time span of above-mentioned Pre-built Video only needs a few minutes, such as 3 minutes, therefore compared to
The mode of questionnaire survey effectively saves the time that personality is estimated, and improves the efficiency that personality is estimated.
In some embodiments, as shown in Fig. 2, personality assessment's method can also include further including constructing the personality to estimate
The step of neural network model:
S210, the personality characteristics for obtaining multiple sample measures persons.
Here, the answer for estimating questionnaire to personality based on multiple sample measures persons obtains the multiple sample measures person's
Personality characteristics；The personality characteristics includes at least one of the following: ofNeuroticism (Neuroticism) score, extropism
(Extroversion) score, opening (Openness) score, agreeableness (Agreeableness) score, doing one's duty property
(Conscientiousness) score.
Here Y value of the personality characteristics as model training.
S220, facial exercises feature of multiple sample measures persons during watching every kind of Pre-built Video is obtained.
S230, the personality characteristics based on the multiple sample measures person and multiple sample measures persons are pre- at every kind of viewing
Facial exercises feature during video processed, training obtain the personality and estimate neural network model.
In the specific implementation, 200 personality measurement persons can be acquired with facial active characteristics, taken out by Bootstrap method
Training set and test set therein are taken, and wherein training set and test set are represented as:
In (Xn, Yn), wherein n is sample number.
Neural network model is estimated using the above-mentioned personality of training set training later, personality is obtained using training and estimates nerve net
Network model carries out personality assessment to the personality measurement person in test set.
Above-mentioned personality, which estimates neural network model, can be depth convolutional neural networks model, the personality obtained using training
It estimates neural network model and carries out any assessment, accuracy rate is higher than 98%.
In some embodiments, a kind of personality assessment's device is provided, the device is corresponding with above-mentioned personality assessment's method,
For executing above-mentioned personality assessment's method.As shown in figure 3, personality assessment's device includes:
Video display module 310, for playing the Pre-built Video with Different Cognitive stimulation angle to personality measurement person；
Feature obtains module 320, for obtaining face of personality measurement person during watching every kind of Pre-built Video respectively
Active characteristics；
Personality prediction module 330, it is pre- at every kind of viewing to personality measurement person for estimating neural network model using personality
The corresponding facial active characteristics of video processed are handled, and the personality estimation results of the personality measurement person are obtained；Wherein, the personality
It is pre- at every kind of viewing using the personality characteristics and multiple sample measures persons of multiple sample measures persons for estimating neural network model
That facial exercises feature training during video processed obtains
Each step in the method for the embodiment of the present invention is the device with the embodiment of the present invention during personality assessment
The step of it is one-to-one, the device of the embodiment of the present invention each step during personality assessment is all contained in implementation of the present invention
In the method for example, therefore, for duplicate part, it is not discussed here.
Above-described embodiment is by the cognitive stimulatory information device of starting plyability (even if personality measurement person watches excitation difference
The Pre-built Video of cognitive activities), the different types of cognitive information processing process of personality measurement person is excited, and record it and recognize in difference
Know corresponding physiology characterization (i.e. facial exercises feature) under process, can effectively distinguish the cognitive information processing of Different Individual
The difference of mechanism can not only guarantee the accuracy that personality is estimated, and can be improved personality to reversely infer personality difference
The efficiency estimated.
It should be noted that, in this document, relational terms such as first and second and the like are used merely to a reality
Body or operation are distinguished with another entity or operation, are deposited without necessarily requiring or implying between these entities or operation
In any actual relationship or order or sequence.Moreover, the terms "include", "comprise" or its any other variant are intended to
Non-exclusive inclusion, so that the process, method, article or equipment including a series of elements is not only wanted including those
Element, but also including other elements that are not explicitly listed, or further include for this process, method, article or equipment
Intrinsic element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that
There is also other identical elements in process, method, article or equipment including the element.
The above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although with reference to the foregoing embodiments
Invention is explained in detail, those skilled in the art should understand that: it still can be to aforementioned each implementation
Technical solution documented by example is modified or equivalent replacement of some of the technical features；And these modification or
Replacement, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of personality assessment's method characterized by comprising
The Pre-built Video with Different Cognitive stimulation angle is played to personality measurement person；
Facial exercises feature of personality measurement person during watching every kind of Pre-built Video is obtained respectively；
Estimate neural network model using personality, to personality measurement person the corresponding facial active characteristics of every kind of Pre-built Video of viewing into
Row processing, obtains the personality estimation results of the personality measurement person；Wherein, it is using more that the personality, which estimates neural network model,
The facial exercises of the personality characteristics of a sample measures person and multiple sample measures persons during watching every kind of Pre-built Video are special
Sign training obtains.
2. the method according to claim 1, wherein the facial exercises feature is that personality measurement person is facial extremely
Facial exercises feature in a few presumptive area.
3. according to the method described in claim 2, it is characterized in that, the facial exercises feature includes at least one of the following:
Facial temperature change of the personality measurement person when watching every kind of Pre-built Video；Personality measurement person is watching every kind of Pre-built Video
When changes in heart rate；Respiratory variations of the personality measurement person when watching every kind of Pre-built Video.
4. according to the method described in claim 3, it is characterized in that, the acquisition personality measurement person is watching every kind of Pre-built Video
Facial exercises feature in the process, comprising:
Face-image of the personality measurement person when watching every kind of Pre-built Video is obtained, the corresponding face K of every kind of Pre-built Video is obtained
Portion's image；Wherein, K is positive integer；
For the corresponding every face-image of every kind of Pre-built Video, it is based on scheming compared with the face-image is corresponding with the face-image
The variation of the gray value of picture determines the corresponding temperature change value of the face-image, heart rate change value and respiratory variations value；
For every kind of Pre-built Video, temperature change value, heart rate change value based on the corresponding every face-image of the Pre-built Video
And respiratory variations value, determine facial exercises feature of personality measurement person during watching the Pre-built Video.
5. according to the method described in claim 4, it is characterized in that, the corresponding movement images of the described face-image are to shoot
The face-image that the previous second of the face-image or latter second beats are taken the photograph.
6. the method according to claim 1, wherein estimating nerve net the method also includes constructing the personality
The step of network model:
Obtain the personality characteristics of multiple sample measures persons；
Obtain facial exercises feature of multiple sample measures persons during watching every kind of Pre-built Video；
Personality characteristics and multiple sample measures persons based on the multiple sample measures person are watching every kind of Pre-built Video process
In facial exercises feature, training obtains the personality and estimates neural network model.
7. according to the method described in claim 6, it is characterized in that, the method estimates personality based on multiple sample measures persons
The answer of questionnaire obtains the personality characteristics of the multiple sample measures person；The personality characteristics includes at least one of the following: nerve
Matter score, extropism score, open score, agreeableness score, doing one's duty property score.
8. according to the method described in claim 6, it is characterized in that, it includes 3 convolution & that the personality, which estimates neural network model,
The Softmax layer of MaxPooling, 1 full linking layer and a p=0.5.
9. the method according to claim 1, wherein the Pre-built Video packet with Different Cognitive stimulation angle
Include at least one following Pre-built Video:
For the Pre-built Video of sensory perception；For the Pre-built Video of working memory；For the Pre-built Video of semantic memory；For height
The Pre-built Video of grade Cognitive Processing；Wherein the higher cognitive includes reasoning from logic, calculating and problem solving.
10. a kind of personality assessment's device characterized by comprising
Video display module, for playing the Pre-built Video with Different Cognitive stimulation angle to personality measurement person；
Feature obtains module, special for obtaining facial exercises of personality measurement person during watching every kind of Pre-built Video respectively
Sign；
Personality prediction module is watching every kind of Pre-built Video to personality measurement person for estimating neural network model using personality
Corresponding face active characteristics are handled, and the personality estimation results of the personality measurement person are obtained；Wherein, the personality estimates mind
It is to watch every kind of Pre-built Video using the personality characteristics and multiple sample measures persons of multiple sample measures persons through network model
What facial exercises feature training in the process obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811590157.9A CN109498039A (en) | 2018-12-25 | 2018-12-25 | Personality assessment's method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811590157.9A CN109498039A (en) | 2018-12-25 | 2018-12-25 | Personality assessment's method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109498039A true CN109498039A (en) | 2019-03-22 |
Family
ID=65754542
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811590157.9A Pending CN109498039A (en) | 2018-12-25 | 2018-12-25 | Personality assessment's method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109498039A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110111810A (en) * | 2019-04-29 | 2019-08-09 | 华院数据技术(上海)有限公司 | Voice personality prediction technique based on convolutional neural networks |
| CN110321440A (en) * | 2019-06-12 | 2019-10-11 | 汕头大学 | A kind of personality assessment's method and system based on emotional state and emotional change |
| CN110652294A (en) * | 2019-09-16 | 2020-01-07 | 清华大学 | Creativity personality trait measuring method and device based on electroencephalogram signals |
| CN114743680A (en) * | 2022-06-09 | 2022-07-12 | 云天智能信息(深圳)有限公司 | Method, device and storage medium for evaluating non-fault |
| CN114870191A (en) * | 2022-07-08 | 2022-08-09 | 北京智精灵科技有限公司 | Cognitive assessment improving method and system based on personality difference |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110015497A1 (en) * | 2009-07-16 | 2011-01-20 | International Business Machines Corporation | System and method to provide career counseling and management using biofeedback |
| US20130281798A1 (en) * | 2012-04-23 | 2013-10-24 | Sackett Solutions & Innovations, LLC | Cognitive biometric systems to monitor emotions and stress |
| CN105022929A (en) * | 2015-08-07 | 2015-11-04 | 北京环度智慧智能技术研究所有限公司 | Cognition accuracy analysis method for personality trait value test |
| CN105869144A (en) * | 2016-03-21 | 2016-08-17 | 常州大学 | Depth image data-based non-contact respiration monitoring method |
| US20160253549A1 (en) * | 2015-02-27 | 2016-09-01 | Leo Ramic | Estimating personal information from facial features |
| CN108245176A (en) * | 2017-12-07 | 2018-07-06 | 江苏大学 | Based on the interactive contactless psychology detection therapeutic device of Internet of Things, system and method |
| CN108345874A (en) * | 2018-04-03 | 2018-07-31 | 苏州欧孚网络科技股份有限公司 | A method of according to video image identification personality characteristics |
| CN108630299A (en) * | 2018-04-27 | 2018-10-09 | 合肥工业大学 | Personality analysis method and system, storage medium based on skin resistance feature |
| US20180366141A1 (en) * | 2017-06-14 | 2018-12-20 | International Business Machines Corporation | Predictive notification of personality shifts for mental illness management |
-
2018
- 2018-12-25 CN CN201811590157.9A patent/CN109498039A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110015497A1 (en) * | 2009-07-16 | 2011-01-20 | International Business Machines Corporation | System and method to provide career counseling and management using biofeedback |
| US20130281798A1 (en) * | 2012-04-23 | 2013-10-24 | Sackett Solutions & Innovations, LLC | Cognitive biometric systems to monitor emotions and stress |
| US20160253549A1 (en) * | 2015-02-27 | 2016-09-01 | Leo Ramic | Estimating personal information from facial features |
| CN105022929A (en) * | 2015-08-07 | 2015-11-04 | 北京环度智慧智能技术研究所有限公司 | Cognition accuracy analysis method for personality trait value test |
| CN105869144A (en) * | 2016-03-21 | 2016-08-17 | 常州大学 | Depth image data-based non-contact respiration monitoring method |
| US20180366141A1 (en) * | 2017-06-14 | 2018-12-20 | International Business Machines Corporation | Predictive notification of personality shifts for mental illness management |
| CN108245176A (en) * | 2017-12-07 | 2018-07-06 | 江苏大学 | Based on the interactive contactless psychology detection therapeutic device of Internet of Things, system and method |
| CN108345874A (en) * | 2018-04-03 | 2018-07-31 | 苏州欧孚网络科技股份有限公司 | A method of according to video image identification personality characteristics |
| CN108630299A (en) * | 2018-04-27 | 2018-10-09 | 合肥工业大学 | Personality analysis method and system, storage medium based on skin resistance feature |
Non-Patent Citations (1)
| Title |
|---|
| M. GAVRILESCU: "Study on determining the Big-Five personality traits of an individual based on facial expressions", 《E-HEALTH AND BIOENGINEERING CONFERENCE》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110111810A (en) * | 2019-04-29 | 2019-08-09 | 华院数据技术(上海)有限公司 | Voice personality prediction technique based on convolutional neural networks |
| CN110111810B (en) * | 2019-04-29 | 2020-12-18 | 华院数据技术(上海)有限公司 | Voice personality prediction method based on convolutional neural network |
| CN110321440A (en) * | 2019-06-12 | 2019-10-11 | 汕头大学 | A kind of personality assessment's method and system based on emotional state and emotional change |
| CN110652294A (en) * | 2019-09-16 | 2020-01-07 | 清华大学 | Creativity personality trait measuring method and device based on electroencephalogram signals |
| CN114743680A (en) * | 2022-06-09 | 2022-07-12 | 云天智能信息(深圳)有限公司 | Method, device and storage medium for evaluating non-fault |
| CN114870191A (en) * | 2022-07-08 | 2022-08-09 | 北京智精灵科技有限公司 | Cognitive assessment improving method and system based on personality difference |
| CN114870191B (en) * | 2022-07-08 | 2022-10-28 | 北京智精灵科技有限公司 | Cognitive assessment improving method and system based on personality difference |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109498039A (en) | Personality assessment's method and device | |
| Rule et al. | Accuracy and consensus in judgments of trustworthiness from faces: behavioral and neural correlates. | |
| Hasson et al. | Enhanced intersubject correlations during movie viewing correlate with successful episodic encoding | |
| MacLin et al. | Race, arousal, attention, exposure and delay: An examination of factors moderating face recognition. | |
| KR102503885B1 (en) | Apparatus and method for predicting human depression level using multi-layer bi-lstm with spatial and dynamic information of video frames | |
| Sripada et al. | Aberrant reward center response to partner reputation during a social exchange game in generalized social phobia | |
| CN107348962B (en) | A kind of personal traits measurement method and equipment based on brain-computer interface technology | |
| Vicaria et al. | Perceptions of rapport across the life span: Gaze patterns and judgment accuracy. | |
| CN109871124A (en) | Emotion virtual reality scenario appraisal procedure based on deep learning | |
| Danvers et al. | Dynamically engaged smiling predicts cooperation above and beyond average smiling levels | |
| Blocker et al. | Not all outgroups are equal: Group type may influence group effect on matching behavior | |
| CN109498038A (en) | Self-closing disease appraisal procedure and device | |
| McDonald et al. | Compassion and values influence marital quality amongst couples in three US states. | |
| Kim et al. | Neural basis of distorted self-face recognition in social anxiety disorder | |
| Wentura et al. | Emotion-specific priming effects with marginally perceptible facial expression primes: Evidence from the “leave-one-out” paradigm. | |
| CN109544043A (en) | Competency appraisal procedure and device | |
| CN109544042A (en) | Ability to lead appraisal procedure and device | |
| Agranov et al. | The process of choice in guessing games | |
| CN109543659A (en) | Risk behavior monitoring and pre-alarming method and system suitable for old user | |
| CN111104815A (en) | Psychological assessment method and device based on emotion energy perception | |
| Reisenzein et al. | On the latency of object recognition and affect: Evidence from temporal order and simultaneity judgments. | |
| CN109635778A (en) | Risk behavior monitoring and pre-alarming method and system suitable for special population | |
| Revol et al. | Episode-contingent experience-sampling designs for accurate estimates of autoregressive dynamics | |
| JP2019101873A (en) | Information processor and program | |
| CN111653326B (en) | Cognitive behavior evaluation method and device for human central nervous system diseases |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190322 |