CN105069318A - Emotion analysis method - Google Patents
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- CN105069318A CN105069318A CN201510577280.7A CN201510577280A CN105069318A CN 105069318 A CN105069318 A CN 105069318A CN 201510577280 A CN201510577280 A CN 201510577280A CN 105069318 A CN105069318 A CN 105069318A
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Abstract
The invention discloses an emotion analysis method, in particular to a method for acquiring psychological emotion information by analyzing the information characteristics of facial expression, voice, pulse and the like. The method includes seven main steps, a current emotion state is judged by capturing the expression and voice information in real time, and emotion fluctuation or emotion information is monitored. Compacted with the prior art, the emotion analysis method has the advantages that the current emotion state of a testee can be acquired through the facial expression and voice, and the emotion analysis method can be widely applied to lie detection analysis, love analysis, recruitment analysis, employee work distribution analysis and other conditions.
Description
Technical field
The present invention relates to a kind of mood analytical approach, particularly utilize a kind of method analyzing the information characteristics acquisition mental emotion information such as facial expression, sound, pulse.
Background technology
Face, as the most direct medium of information transmission, plays very important role, and we can directly obtain face information by eyes can analyze perception face mood by brain again.But carry out this observation-analytical work for a long time, people are easy to just be absorbed in fatigue thus cause the accuracy rate of analysis greatly to decline.If computing machine can be made to possess identical ability, just can realize full-time analysis in theory, thus provide people to analyze data accurately and reliably in a large number, thus provide support for our decision-making.Moreover, can carry out multiple analysis after the basis of face perception increases perception of sound, pulse perception and dermatopolyneuritis comparison, by above data, such as selecting to detect a lie to analyze just show whether tested object lies; Selection mutual affection of making love is analysed and is just shown whether tested object likes you by above data; Select to do enterprises recruitment just the draws tested object heart of respecting work by above data, loyalty degree.
Summary of the invention
The present invention is directed to deficiency of the prior art, provide a kind of mood analytical approach, can obtain the current emotional state of test person by facial expression and sound, the present invention can be widely used in the situations such as analysis of detecting a lie, love analysis, recruitment analysis, employee work distribution analysis.
In order to solve the problems of the technologies described above, the present invention is solved by following technical proposals: a kind of mood analytical approach, need to utilize hardware module to realize its function, described hardware module comprises video acquisition module and database module, it is characterized by and comprises following step:
Steps A): setting data library module, makes according to having 8 standards expression in library module, 8 standard scale mutual affections be not glad, happy, flat, irritable, detest, anger, anxiety, sad, each standard is expressed one's feelings and is had at least 20 expression figure;
Step B): according to the weighted value of each standard expression of different application scene setting, it arranges rule: weighted value is 1 to 100, benchmark weight is 50, the standard deviated from application scenarios weighted value of expressing one's feelings is higher and more to deviate from weighted value higher than benchmark weight, and the standard expression weighted value corresponding to application scenarios is lower and more respective weights numerical value is lower than benchmark weight;
Step C): video acquisition module utilizes facial geometric feature to get a method and captures the actual expression of tested person, the point position captured is between 20 o'clock to 25 o'clock, contrast one by one with the expression figure in database module after crawl, and filter out the institute espressiove figure of similar more than 10 an of position, then carry out postsearch screening, each standard is expressed one's feelings and only has at most an expression figure;
Step D): if the expression figure quantity filtered out is greater than 3, then continue to filter out similar three figure at most in a position;
Step e): according to the actual value of each reality expression of different application scene setting, it arranges rule: actual value is 1 to 100, benchmark is divided into 50, the ratio of the crawl point that similitude and the reality of expression figure are expressed one's feelings all is actual ratio, actual value is the product addition of weighted value and actual ratio in each expression figure scene corresponding to it, if actual value is more than 100, gets 100;
Step F): once analyze and can comprise several standard paragraphs, the time of each standard paragraphs is between 30 seconds to 30 minutes, in standard paragraphs, each video acquisition module captures the multiple being spaced apart 0.01 second of actual expression, and minimum interval is 0.01 second, largest interval is 0.1 second, and the grabbing interval of same standard paragraphs does not change;
Step G): after one time standard paragraphs terminates, can obtain the set of actual value, the set of actual value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, and whether the mood drawing test person therewith application scenarios deviates from.
In technique scheme, preferably, described hardware module also comprises sound acquisition module and sound analysis module, sound acquisition module utilizes recording to collect tested person's acoustic information and sound acquisition module and video acquisition module are operated in same standard paragraphs simultaneously, sound analysis module obtains speech frequency by analyzing acoustic information and arranges sound weight, it arranges rule: sound normal frequency then sound weight be 0, more deviate from normal frequency sound weight higher, sound weight is the highest is no more than 20, also be provided with mood value, mood value is current actual value in step e and current time node sound weight sum, if mood value, more than 100, gets 100, after one time standard paragraphs terminates, the set of mood value can be obtained, the set of mood value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, whether the mood drawing test person therewith application scenarios deviates from.
In technique scheme, preferably, described hardware module also comprises sound acquisition module and sound analysis module, sound acquisition module utilizes recording to collect tested person's acoustic information and sound acquisition module and video acquisition module are operated in same standard paragraphs simultaneously, sound analysis module obtains speech frequency by analyzing acoustic information and arranges sound weight, it arranges rule: sound normal frequency then sound weight be 20, more deviate from normal frequency sound weight lower, sound weight is the highest is no more than 20, also be provided with mood value, mood value is the sound weight sum of current actual value in step e and current time node, if mood value, more than 100, gets 100, after one time standard paragraphs terminates, the set of mood value can be obtained, the set of mood value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, whether the mood drawing test person therewith application scenarios deviates from.
In technique scheme, preferably, the some position captured in described step C is 25 points, and carries out postsearch screening again after filtering out the institute espressiove figure of similar more than 15 an of position.
In technique scheme, preferably, if the expression figure quantity filtered out in described step D is greater than 2, then continue to filter out similar two figure at most in a position.
In technique scheme, preferably, described video acquisition module is camera.
In technique scheme, preferably, described sound acquisition module is microphone.
The present invention is a kind of mood analytical approach, and the main expression that utilizes judges, the auxiliary sound that utilizes judges to identify the mood that tested person is current, finds that the anxious state of mind of tested person is to determine whether current tested person tells a lie in conjunction with practical application scene.This analysis is to the analysis that expression data and voice data carry out within a period of time, the maximum feature of this analysis is that the expression captured each time all will contrast with all pictures in database, and draw mood value through weighted calculation, if mood value just illustrates that above benchmark weight deviating from appears in measured's mood and the situation of presence, then illustrates that below benchmark weight mood is normal.Repeatedly can carry out standard paragraphs analysis in once total test, in conjunction with the conclusion that all standard paragraphs draw, just can obtain the test person anxious state of mind concrete when acceptance test.This method is applicable to detect a lie analysis, love analysis, recruitment analysis, employee work distribution analysis etc.
Compared with prior art, the invention has the beneficial effects as follows: can obtain the current emotional state of test person by facial expression and sound, the present invention can be widely used in the situations such as analysis of detecting a lie, love analysis, recruitment analysis, employee work distribution analysis.
Embodiment
Below in conjunction with embodiment, the present invention is described in further detail.
Embodiment 1: when judging for detecting a lie, first set happiness, happiness, flat, irritable, detest, angry, anxiety, sad weighted value be respectively 30,30,20,70,60,60,80,40; : sound normal frequency then sound weight be 0, more deviate from normal frequency sound weight higher, sound weight is the highest is no more than 20, the setting of the weighted value of sound weighted frequency will with tested person's actual sound frequency dependence because the audible frequency difference of different sexes and age sound is larger.Then by camera and microphone, the collection of facial expression and sound is carried out to tested person.A mood analysis comprises 8 standard paragraphs, wherein each standard paragraphs duration 5 minutes, and in standard paragraphs, expression captures the time interval is 0.2 second.Expression captures and adopts facial geometric feature to get a method, gets a little 25 at every turn.Get the expression figure put in rear captured expression and database module to contrast one by one at every turn, and filter out the institute espressiove figure of similar more than 15 an of position, then postsearch screening is carried out, each standard is expressed one's feelings and only has at most an expression figure, if the expression figure quantity filtered out is greater than 2, then continuing to filter out similar two figure at most in a position, then by calculating actual value during each crawl, 1500 actual values can be obtained in a standard paragraphs for this reason.1500 mood values that a standard paragraphs is last are drawn again by the sound weight be added corresponding to the voice data of same time point microphone acquisition.These 1500 mood values and benchmark weight are depicted as curve and straight line (wherein X-axis is the time, and Y-axis is weighted value is 1 to 100) respectively.Then jointly analyze the functional image of 8 standard paragraphs, by comparing deviating from degree and deviate from the time and determining whether tested person tells a lie of mood value curve and benchmark weight, when the mood value curve most of the time than benchmark weight height time, tested person's lie; The mood value curve most of the time, time lower than benchmark weight, tested person did not tell a lie, and can also analyze separately the functional image in a standard paragraphs, obtain the emotional state of tested person in a standard paragraphs.
Embodiment 2: during for love state, first set happiness, happiness, flat, irritable, detest, angry, nervous, sad weighted value is respectively 15,15,60,70,90,80,20,70; : sound normal frequency then sound weight be 20, more deviate from normal frequency sound weight lower, sound weight is the highest to be no more than 20 and to be minimumly no more than 0, sound weighted frequency weighted value setting will with tested person's actual sound frequency dependence because the audible frequency difference of different sexes and age sound is larger.Then by camera and microphone, the collection of facial expression and sound is carried out to tested person.A mood analysis comprises 2 standard paragraphs, wherein each standard paragraphs duration 2 minutes, and in standard paragraphs, expression captures the time interval is 0.1 second.Expression captures and adopts facial geometric feature to get a method, gets a little 20 at every turn.Get the expression figure put in rear captured expression and database module to contrast one by one at every turn, and filter out the institute espressiove figure of similar more than 15 an of position, then postsearch screening is carried out, each standard is expressed one's feelings and only has at most an expression figure, if the expression figure quantity filtered out is greater than 2, then continuing to filter out similar two figure at most in a position, then by calculating actual value during each crawl, 1200 actual values can be obtained in a standard paragraphs for this reason.1200 mood values that a standard paragraphs is last are drawn again by the sound weight be added corresponding to the voice data of same time point microphone acquisition.These 1200 mood values and benchmark weight are depicted as curve and straight line (wherein X-axis is the time, and Y-axis is weighted value is 1 to 100) respectively.Then jointly analyze the functional image of 2 standard paragraphs, by comparing deviating from degree and deviate from the time and determining whether tested person falls in love of mood value curve and benchmark weight, when the mood value curve most of the time than benchmark weight height time, tested person does not like; The mood value curve most of the time, time lower than benchmark weight, tested person fell in love, and can also analyze separately the functional image in a standard paragraphs, obtain the emotional state of tested person in a standard paragraphs.
Moreover, with when once analyzing, the time span of each standard paragraphs can be different, and in each standard paragraphs, the grabbing interval of expression also can be different.This method can also be applicable to employment interview analysis, post distributes the scenes such as analysis, only need the weighted value that pre-set standard is expressed one's feelings before testing, this method can also carry out independent Expression analysis without the need to carrying out phonetic analysis simultaneously, if only have independent Expression analysis, actual value is depicted as curve and benchmark weight line contrasts, moreover this method can also increase respiratory rate mensuration, skin surface electrometric determination, so only needs that determination data is converted into weighted value and just can compare.Generally speaking, this method needs hardware assist, can pass through computer, and the electronic products such as mobile phone use this method to reach and analyze tested person's mood volume object, and regular this method can be written as an APP for mobile phone.
Claims (7)
1. a mood analytical approach, need to utilize hardware module to realize its function, described hardware module comprises video acquisition module and database module, it is characterized by and comprise following step: steps A): setting data library module, make according to having 8 standard expressions in library module, 8 standard scale mutual affections be not glad, happy, flat, irritable, detest, angry, nervous, sad, each standard expression has at least 20 expression figure; Step B): according to the weighted value of each standard expression of different application scene setting, it arranges rule: weighted value is 1 to 100, benchmark weight is 50, the standard deviated from application scenarios weighted value of expressing one's feelings is higher and more to deviate from weighted value higher than benchmark weight, and the standard expression weighted value corresponding to application scenarios is lower and more respective weights numerical value is lower than benchmark weight; Step C): video acquisition module utilizes facial geometric feature to get a method and captures the actual expression of tested person, the point position captured is between 20 o'clock to 25 o'clock, contrast one by one with the expression figure in database module after crawl, and filter out the institute espressiove figure of similar more than 10 an of position, then carry out postsearch screening, each standard is expressed one's feelings and only has at most an expression figure; Step D): if the expression figure quantity filtered out is greater than 3, then continue to filter out similar three figure at most in a position; Step e): according to the actual value of each reality expression of different application scene setting, it arranges rule: actual value is 1 to 100, benchmark is divided into 50, the ratio of the crawl point that similitude and the reality of expression figure are expressed one's feelings all is actual ratio, actual value is the product addition of weighted value and actual ratio in each expression figure scene corresponding to it, if actual value is more than 100, gets 100; Step F): once analyze and can comprise several standard paragraphs, the time of each standard paragraphs is between 30 seconds to 30 minutes, in standard paragraphs, each video acquisition module captures the multiple being spaced apart 0.01 second of actual expression, and minimum interval is 0.01 second, largest interval is 0.1 second, and the grabbing interval of same standard paragraphs does not change; Step G): after one time standard paragraphs terminates, can obtain the set of actual value, the set of actual value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, and whether the mood drawing test person therewith application scenarios deviates from.
2. a kind of mood analytical approach according to claim 1, it is characterized by, described hardware module also comprises sound acquisition module and sound analysis module, sound acquisition module utilizes recording to collect tested person's acoustic information and sound acquisition module and video acquisition module are operated in same standard paragraphs simultaneously, sound analysis module obtains speech frequency by analyzing acoustic information and arranges sound weight, it arranges rule: sound normal frequency then sound weight be 0, more deviate from normal frequency sound weight higher, sound weight is the highest is no more than 20, also be provided with mood value, mood value is the sound weight sum of current actual value in step e and current time node, if mood value, more than 100, gets 100, after one time standard paragraphs terminates, the set of mood value can be obtained, the set of mood value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, whether the mood drawing test person therewith application scenarios deviates from.
3. a kind of mood analytical approach according to claim 1, it is characterized by, described hardware module also comprises sound acquisition module and sound analysis module, sound acquisition module utilizes recording to collect tested person's acoustic information and sound acquisition module and video acquisition module are operated in same standard paragraphs simultaneously, sound analysis module obtains speech frequency by analyzing acoustic information and arranges sound weight, it arranges rule: sound normal frequency then sound weight be 20, more deviate from normal frequency sound weight lower, sound weight is the highest is no more than 20, also be provided with mood value, mood value is the sound weight sum of current actual value in step e and current time node, if mood value, more than 100, gets 100, after one time standard paragraphs terminates, the set of mood value can be obtained, the set of mood value and benchmark weight ratio draw the emotional reactions of test person at this standard paragraphs more afterwards, whether the mood drawing test person therewith application scenarios deviates from.
4. a kind of mood analytical approach according to claim 1, is characterized by, and the some position captured in described step C is 25 points, and carries out postsearch screening again after filtering out the institute espressiove figure of similar more than 15 an of position.
5. a kind of mood analytical approach according to claim 1, is characterized by, if the expression figure quantity filtered out in described step D is greater than 2, then continues to filter out similar two figure at most in a position.
6. a kind of mood analytical approach according to claim 1, is characterized by, and described video acquisition module is camera.
7. a kind of mood analytical approach according to claim 1, is characterized by, and described sound acquisition module is microphone.
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Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105496371A (en) * | 2015-12-21 | 2016-04-20 | 中国石油大学(华东) | Method for emotion monitoring of call center service staff |
| CN106909907A (en) * | 2017-03-07 | 2017-06-30 | 佛山市融信通企业咨询服务有限公司 | A kind of video communication sentiment analysis accessory system |
| CN107625527A (en) * | 2016-07-19 | 2018-01-26 | 杭州海康威视数字技术股份有限公司 | A kind of lie detecting method and device |
| CN107669282A (en) * | 2017-11-19 | 2018-02-09 | 济源维恩科技开发有限公司 | A lie detector based on face recognition |
| CN107714056A (en) * | 2017-09-06 | 2018-02-23 | 上海斐讯数据通信技术有限公司 | A kind of wearable device of intellectual analysis mood and the method for intellectual analysis mood |
| CN108014498A (en) * | 2016-10-31 | 2018-05-11 | 北京金山云网络技术有限公司 | One kind game method of calibration and device |
| TWI646438B (en) * | 2017-04-25 | 2019-01-01 | 元智大學 | Emotion detection system and method |
| CN109241864A (en) * | 2018-08-14 | 2019-01-18 | 中国平安人寿保险股份有限公司 | Emotion prediction technique, device, computer equipment and storage medium |
| CN109920515A (en) * | 2019-03-13 | 2019-06-21 | 商洛学院 | A kind of mood dredges interaction systems |
| CN110111874A (en) * | 2019-04-18 | 2019-08-09 | 上海图菱新能源科技有限公司 | Artificial intelligence Emotion identification management migrates interactive process and method |
| RU2703969C1 (en) * | 2018-12-13 | 2019-10-22 | Общество с Ограниченной Ответственностью "Хидбук Клауд" | Method and system for evaluating quality of customer service based on analysis of video and audio streams using machine learning tools |
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| CN105496371A (en) * | 2015-12-21 | 2016-04-20 | 中国石油大学(华东) | Method for emotion monitoring of call center service staff |
| CN107625527A (en) * | 2016-07-19 | 2018-01-26 | 杭州海康威视数字技术股份有限公司 | A kind of lie detecting method and device |
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| RU2703969C1 (en) * | 2018-12-13 | 2019-10-22 | Общество с Ограниченной Ответственностью "Хидбук Клауд" | Method and system for evaluating quality of customer service based on analysis of video and audio streams using machine learning tools |
| CN109920515A (en) * | 2019-03-13 | 2019-06-21 | 商洛学院 | A kind of mood dredges interaction systems |
| CN110111874A (en) * | 2019-04-18 | 2019-08-09 | 上海图菱新能源科技有限公司 | Artificial intelligence Emotion identification management migrates interactive process and method |
| WO2021190086A1 (en) * | 2020-03-26 | 2021-09-30 | 深圳壹账通智能科技有限公司 | Face-to-face examination risk control method and apparatus, computer device, and storage medium |
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