CN109199411A - Case insider's recognition methods based on Model Fusion - Google Patents
Case insider's recognition methods based on Model Fusion Download PDFInfo
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- CN109199411A CN109199411A CN201811135018.7A CN201811135018A CN109199411A CN 109199411 A CN109199411 A CN 109199411A CN 201811135018 A CN201811135018 A CN 201811135018A CN 109199411 A CN109199411 A CN 109199411A
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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
- A61B5/16—Devices for psychotechnics; Testing reaction times ; Devices for evaluating the psychological state
- A61B5/165—Evaluating the state of mind, e.g. depression, anxiety
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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
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1103—Detecting eye twinkling
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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
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
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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
- G06V40/18—Eye characteristics, e.g. of the iris
- G06V40/193—Preprocessing; Feature extraction
Abstract
Case insider's recognition methods based on Model Fusion that the invention discloses a kind of, includes the following steps, extracts 32 dimension eye movement characteristics of each testee when watching single picture；Based on 32 dimension eye movement characteristics training SVM model A, to identify speech confidence level of each testee in single picture, and probability f of each testee in single picture is exported1(xi) and f2(xi)；Extract 110 dimension eye movement characteristics of each testee when watching combination picture；Based on 110 dimension eye movement characteristics training SVM Model Bs, to identify speech confidence level of each testee in combination picture, and probability g of each testee in combination picture is exported1(xi) and g2(xi)；With multiplication rule, the classifier probability of SVM model A and B is merged, joint probability is obtained, taking the classification of the maximum probability of each testee is the last result of decision.The present invention can effectively inhibit anti-means of detecting a lie, and improve efficiency of algorithm.
Description
Technical field
The present invention relates to criminal investigations to inquest analysis technical field, and in particular to a kind of case insider knowledge based on Model Fusion
Other method.
Background technique
Under criminal investigation background, the key for inquesting suspect is to evaluate and test the abnormal emotion of suspect,
I.e. so-called " detecting a lie ".Interrogator judges its psychological condition by observing the performance of suspect, and in its speech
Loophole is broken through the mental line of defense of suspect, it is forced to say the truth of the matter using some hearing skills.But normal person
Ability of detecting a lie near conjecture, usually rely on intuition judgement, therefore, accuracy rate only it is higher than random chance little by little, Er Qietong
Often will also be by a small number of hearing experts having wide experience, this is clearly time-consuming and inefficient.
Since psychology variation of the people when lying can cause some physiological parameters (such as skin pricktest, heartbeat, blood pressure, breathing brain
Electric wave, sound etc.) variation, therefore, it is a kind of effective for assessing measured by detecting these variations whether case is known
Supplementary means.In the research of early stage, case is carried out to suspect using polygraph and knows identify it is most common
One of method.But physical signs used by polygraph is often subject to the influence of various factors, the body machine including people
Energy, psychological condition, task stimulus intensity and the person's of detecting a lie ability etc..
In recent years, with the development of brain cognition neural technology, researcher can directly observe interior when mentition occurs
The nervous activity of portion related brain areas, it is more objective compared with by traditional lie-detection technology of external physiologic activity change, more can
Movable inherent laws of telling a lie are disclosed, one of lie-detection technology developing direction is become.However, professional equipment needed for such technology is huge
It is big and valuable, the practicality is limited, and there are corresponding anti-means of detecting a lie to influence test result.
It follows that the lie-detection technology based on above-mentioned physiological signal, is above urgently improved there are still some actually practical
Place, main reason is that: 1) degree of cooperation of measured, most of physiology lie detecting method, acquisition physiology ginseng
Whens number such as electrocardio, skin potential activity, blood pressure, brain wave etc., require to paste at the electrode of contact-sensing instrument or sensor patch
In the body somewhere of tested person, need measured subjective must cooperate, otherwise, concealed anti-lie-detection technology can be used in measured
(such as dynamic toe is given free rein to fancy) carrys out disturbed test result；2) concealment of measurement means, emotional stress have weight in detecting a lie
The research significance wanted, it will be clear, however, that test equipment can inherently cause certain extra pressure, feelings in this case to patient
Thread fluctuation bring measurement influences to be difficult to estimate.Although voice lie detection technology has certain concealment, voice is easy
It by external environment influence, for example dialect, accent, chips in, technical difficulty is larger, studies at present at the early-stage.Therefore, effectively
The characteristics of detecting a lie, it is untouchable to have, strong concealment, and analyzed signal is convenient for acquisition and processing.
It can be seen that above-mentioned existing case insider recognition methods, there are still there is inconvenient and defect, and be urgently subject into
One step is improved.In order to solve the problems, such as that case insider's recognition methods exists, those skilled in the relevant arts there's no one who doesn't or isn't painstakingly come
Seek solution, but has no that applicable method is developed completion always for a long time, and general case insider's recognizer
Again cannot be appropriate solve the above problems, this is clearly the problem of related dealer is suddenly to be solved.Relative to other anti-means of detecting a lie
For, some eye movement indexs are not known by people's will to be controlled, and control certain eye movement indexs intentionally will appear Indexes Abnormality instead.Therefore,
Carrying out case insider identification with eye movement index has certain feasibility, current problem to be solved when how to realize.
Summary of the invention
In order to overcome case insider recognition methods in the prior art, existing inconvenient and defect.It is of the invention based on mould
Case insider's recognition methods of type fusion, solves the examined people's degree of cooperation of case insider recognition methods in the prior art
Restrict, test method it is not concealed, the technical problems such as testing efficiency is low carry out case insider identification using eye movement data, can be with
Effectively inhibit anti-means of detecting a lie, and using 32 eye movement characteristics and 110 dimension eye movement characteristics Model Fusion algorithms, is effectively utilized difference
Subject Psychological Manifestations under mode improve efficiency of algorithm, and the ingenious novelty of method, identification accuracy is high, has good application
Prospect.
In order to achieve the above object, the technical scheme adopted by the invention is that:
A kind of case insider's recognition methods based on Model Fusion, includes the following steps,
Step (A) extracts 32 dimension eye movement characteristics of each testee when watching single picture；
Step (B), based on 32 dimension eye movement characteristics training SVM model A, to identify each testee single
Speech confidence level when picture, and export probability f of each testee in single picture1(xi) and f2(xi), wherein xiGeneration
I-th of testee of table, f1And f2Respectively indicating i-th of testee is insider in single picture or is not insider
Probability；
Step (C) extracts 110 dimension eye movement characteristics of each testee when watching combination picture；
Step (D), based on 110 dimension eye movement characteristics training SVM Model Bs, to identify that each testee is combining
Speech confidence level when picture, and export probability g of each testee in combination picture1(xi) and g2(xi), wherein xiGeneration
I-th of testee of table, g1And g2Respectively indicating i-th of testee is insider in combination picture or is not insider
Probability；
Step (E) merges the classifier probability of SVM model A and B, obtains joint probability with multiplication rule
f1(xi)g1(xi) and f2(xi)g2(xi), the classification for taking each testee's maximum probability corresponding is the last result of decision.
Case insider's recognition methods above-mentioned based on Model Fusion, step (A), the 32 dimension eye movement characteristics, including
Blink statistic 6: number of winks, frequency of wink, blink total duration, duration of averagely blinking, blink duration are maximum
Value, blink duration minimum value；Watch statistic 11 attentively: fixation times, gaze frequency, when watching total duration, average fixation attentively
It is long, watch duration maximum value attentively, watch duration minimum value attentively, always watching deviation, average fixation deviation, maximum attentively and watch deviation, minimum note attentively
Depending on deviation, pan path length；Twitching of the eyelid statistic 15: twitching of the eyelid number, twitching of the eyelid frequency, twitching of the eyelid total duration, twitching of the eyelid be averaged duration,
Twitching of the eyelid duration maximum value, twitching of the eyelid duration minimum value, total twitching of the eyelid amplitude, average eye skip frame degree, maximum twitching of the eyelid amplitude, minimum twitching of the eyelid
Amplitude, total saccadic speed, average saccadic speed, twitching of the eyelid maximum speed, twitching of the eyelid minimum speed, average twitching of the eyelid delay time.
Case insider's recognition methods above-mentioned based on Model Fusion, step (C), the 110 dimension eye movement characteristics refer to
There are 11 dimensional features in the area combination picture Shang Fen10Ge, each area, including fixation time summation Net Dwell Time in region of interest, emerging
Watched attentively in interesting area with the sum of twitching of the eyelid time Dwell Time, into the sum of the twitching of the eyelid time of region of interest and Dwell time Glance
The sum of Duration, the twitching of the eyelid time for leaving region of interest and Glance Duration, watch duration attentively for the first time, twitching of the eyelid is from other areas
Domain jump to fixation time summation Net Dwell Time in the number, fixation times, region of interest in the region account for total time ratio,
Watch attentively in region of interest and accounts for the ratio of total time with the sum of twitching of the eyelid time Dwell Time, always watches duration attentively, always watches attentively and account for total time
Ratio.
The beneficial effects of the present invention are: case insider's recognition methods of the invention based on Model Fusion, solves existing
The skills such as the examined people's degree of cooperation of case insider's recognition methods in technology restricts, test method is not concealed, and testing efficiency is low
Art problem carries out case insider identification using eye movement data, can effectively inhibit anti-means of detecting a lie, and use 32 eye movement characteristics
With 110 dimension eye movement characteristics Model Fusion algorithms, the subject Psychological Manifestations being effectively utilized under different mode improve algorithm effect
Rate, the ingenious novelty of method, identification accuracy is high, has a good application prospect.
Detailed description of the invention
Fig. 1 is the flow chart of the abnormal emotion recognition methods of the invention based on eye movement data analysis.
Specific embodiment
Below in conjunction with Figure of description, the present invention is further illustrated.
As shown in Figure 1, case insider's recognition methods of the invention based on Model Fusion, includes the following steps,
Step (A) extracts 32 dimension eye movement characteristics of each testee when watching single picture, 32 Wei Yandongte
Sign, including blink statistic 6: when number of winks, frequency of wink, blink total duration, averagely blink duration, blink continue
Long maximum value, blink duration minimum value；Watch statistic 11 attentively: fixation times, gaze frequency watch total duration attentively, are average
Watch attentively duration, watch attentively duration maximum value, watch attentively duration minimum value, always watch attentively deviation, average fixation deviation, maximum watch attentively deviation,
Minimum watches deviation, pan path length attentively；Twitching of the eyelid statistic 15: twitching of the eyelid number, twitching of the eyelid frequency, twitching of the eyelid total duration, twitching of the eyelid are flat
Equal duration, twitching of the eyelid duration maximum value, twitching of the eyelid duration minimum value, total twitching of the eyelid amplitude, average eye skip frame degree, maximum twitching of the eyelid amplitude,
When minimum twitching of the eyelid amplitude, total saccadic speed, average saccadic speed, twitching of the eyelid maximum speed, twitching of the eyelid minimum speed, average twitching of the eyelid delay
Between；
Step (B), based on 32 dimension eye movement characteristics training SVM (SVM) model A, to identify that each testee exists
Speech confidence level when single picture, and export probability f of each testee in single picture1(xi) and f2(xi),
In, xiRepresent i-th of testee, f1And f2Respectively indicating i-th of testee is insider in single picture or is not
The probability of insider；
Step (C) extracts 110 dimension eye movement characteristics of each testee when watching combination picture, the 110 dimension eye movement
Feature refers to that there are 11 dimensional features in the area combination picture Shang Fen10Ge, each area, including fixation time summation Net Dwell in region of interest
Watch attentively in Time, region of interest with the sum of twitching of the eyelid time Dwell Time, into the twitching of the eyelid time of region of interest and Dwell time it
With Glance Duration, the sum of the twitching of the eyelid time and the Glance Duration that leave region of interest, watch duration, twitching of the eyelid attentively for the first time
When jumping to that fixation time summation Net Dwell Time accounts for total in the number, fixation times, region of interest in the region from other regions
Between ratio, watch attentively in region of interest and to account for the ratio of total time with the sum of twitching of the eyelid time Dwell Time, always watch duration attentively, always watch attentively
Account for the ratio of total time；
Step (D), based on 110 dimension eye movement characteristics training SVM Model Bs, to identify that each testee is combining
Speech confidence level when picture, and export probability g of each testee in combination picture1(xi) and g2(xi), wherein xiGeneration
I-th of testee of table, g1And g2Respectively indicating i-th of testee is insider in combination picture or is not insider
Probability；
Step (E) merges the classifier probability of SVM model A and B, obtains joint probability with multiplication rule
f1(xi)g1(xi) and f2(xi)g2(xi), taking corresponding classification when the maximum probability of each testee is the last result of decision,
The last result of decision is case insider's recognition result.
Case insider's recognition methods based on Model Fusion of the invention, recognition effect is as shown in table 1, compares algorithm packet
SVM (SVM), artificial neural network (ANN), decision tree (DT) and random forest (RF) are included, as can be known from Table 1, for
For single picture, RF algorithm highest, ANN algorithm is minimum；And for combination picture, ANN algorithm highest, RF algorithm is most
Low, comparatively, the discrimination of SVM algorithm and DT algorithm is moderate, is suitble to two kinds of models, after Model Fusion strategy, support
The discrimination of vector machine (SVM) can reach 86.1%, and 9.2% He is respectively increased for the highest discrimination of both of which
17.6%, therefore, with multiplication rule, the classifier probability of SVM model A and B is merged, joint probability f is obtained1(xi)
g1(xi) and f2(xi)g2(xi), taking corresponding classification when the maximum probability of each testee is the last result of decision, can
The accuracy of the result of decision is greatly improved, moreover, ten division of selection of 32 eye movement characteristics of the invention and 110 dimension eye movement characteristics
Reason, can accurately react all indexs in eye moving process.
Algorithm discrimination comparison under 1 both of which of table
In conclusion case insider's recognition methods of the invention based on Model Fusion, solves case in the prior art
The technical problems such as the examined people's degree of cooperation of part insider's recognition methods restricts, test method is not concealed, and testing efficiency is low, use
Eye movement data carries out case insider identification, can effectively inhibit anti-means of detecting a lie, and using 32 eye movement characteristics and 110 dimension eye movements
Characteristic model blending algorithm, the subject Psychological Manifestations being effectively utilized under different mode improve efficiency of algorithm, and method is ingenious new
Grain husk, identification accuracy is high, has a good application prospect.
Basic principles and main features and advantage of the invention have been shown and described above.The technical staff of the industry should
Understand, the present invention is not limited to the above embodiments, and the above embodiments and description only describe originals of the invention
Reason, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes and improvements
It all fall within the protetion scope of the claimed invention.The claimed scope of the invention is by appended claims and its equivalent circle
It is fixed.
Claims (3)
1. case insider's recognition methods based on Model Fusion, it is characterised in that: include the following steps,
Step (A) extracts 32 dimension eye movement characteristics of each testee when watching single picture；
Step (B), based on 32 dimension eye movement characteristics training SVM model A, to identify each testee in single picture
When speech confidence level, and export probability f of each testee in single picture1(xi) and f2(xi), wherein xiRepresent
I testee, f1And f2Respectively indicating i-th of testee is insider in single picture or is not the general of insider
Rate；
Step (C) extracts 110 dimension eye movement characteristics of each testee when watching combination picture；
Step (D), based on 110 dimension eye movement characteristics training SVM Model Bs, to identify each testee in combination picture
When speech confidence level, and export probability g of each testee in combination picture1(xi) and g2(xi), wherein xiRepresent
I testee, g1And g2Respectively indicating i-th of testee is insider in combination picture or is not the general of insider
Rate；
Step (E) merges the classifier probability of SVM model A and B, obtains joint probability f with multiplication rule1(xi)
g1(xi) and f2(xi)g2(xi), the classification for taking each testee's maximum probability corresponding is the last result of decision.
2. case insider's recognition methods according to claim 1 based on Model Fusion, it is characterised in that: step (A),
The 32 dimension eye movement characteristics, including blink statistic 6: number of winks, frequency of wink, blink total duration, average blink
Duration, blink duration maximum value, blink duration minimum value；Watch statistic 11 attentively: fixation times, gaze frequency,
Watch total duration, average fixation duration attentively, watches duration maximum value attentively, watches that duration minimum value, always to watch deviation, average fixation attentively inclined attentively
Difference, maximum watches deviation attentively, minimum watches deviation attentively, pan path length；Twitching of the eyelid statistic 15: twitching of the eyelid number, twitching of the eyelid frequency, eye
Total duration, twitching of the eyelid is jumped to be averaged duration, twitching of the eyelid duration maximum value, twitching of the eyelid duration minimum value, total twitching of the eyelid amplitude, average eye skip frame
Degree, maximum twitching of the eyelid amplitude, minimum twitching of the eyelid amplitude, total saccadic speed, average saccadic speed, twitching of the eyelid maximum speed, the minimum speed of twitching of the eyelid
Degree, average twitching of the eyelid delay time.
3. case insider's recognition methods according to claim 1 based on Model Fusion, it is characterised in that: step (C),
The 110 dimension eye movement characteristics refer to that there are 11 dimensional features, including fixation time in region of interest in the area combination picture Shang Fen10Ge, each area
Watched attentively in summation Net Dwell Time, region of interest with the sum of twitching of the eyelid time Dwell Time, into the twitching of the eyelid time of region of interest
With the sum of the sum of Dwell time Glance Duration, the twitching of the eyelid time for leaving region of interest and Glance Duration, first
It is secondary to watch duration, twitching of the eyelid attentively and jump to fixation time summation Net in the number, fixation times, region of interest in the region from other regions
Dwell Time account for the ratio of total time, watch attentively in region of interest with the sum of twitching of the eyelid time Dwell Time account for total time ratio,
Always watch duration attentively, always watch the ratio for accounting for total time attentively.
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| CN110956143A (en) * | 2019-12-03 | 2020-04-03 | 交控科技股份有限公司 | Abnormal behavior detection method and device, electronic equipment and storage medium |
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| CN110693509A (en) * | 2019-10-17 | 2020-01-17 | 中国人民公安大学 | Case correlation determination method and device, computer equipment and storage medium |
| CN110693509B (en) * | 2019-10-17 | 2022-04-05 | 中国人民公安大学 | Case correlation determination method and device, computer equipment and storage medium |
| CN110956143A (en) * | 2019-12-03 | 2020-04-03 | 交控科技股份有限公司 | Abnormal behavior detection method and device, electronic equipment and storage medium |
| CN111568367A (en) * | 2020-05-14 | 2020-08-25 | 中国民航大学 | Method for identifying and quantifying eye jump invasion |
| CN111568367B (en) * | 2020-05-14 | 2023-07-21 | 中国民航大学 | Method for identifying and quantifying eye jump invasion |
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