JP6377545B2 - Personal identification method, personal identification device, program - Google Patents

Description

  The present invention relates to a personal identification method, a personal identification device, and a program for identifying an individual based on eye movements.

  Conventionally, in experiments that track characters and figures with gaze, eye movement behavior profiles (EmBP: Eye-movement Behavior Profiles) such as gaze time, saccade frequency, and amplitude differ from person to person and are related to attention ability. The knowledge that it is doing is known (refer nonpatent literature 1).

William Poynter, Megan Barber, Jason Inman, Coral Wiggins, "Individuals exhibit idiosyncratic eye-movement behavior profiles across tasks", Vision Research, 2013, Volume 89, Pages 32-38.

  As described above, although it is known that there is an individual difference in EmBP, a method for identifying an individual based on a feature amount of eye movement including EmBP is not known. In addition, EmBP is an index calculated under a specific eye tracking task, and it is assumed that there is a saccade eye movement that is a relatively large eye movement. Therefore, EmBP is not necessarily an index that can be applied to a person in a resting state or a gaze state, and it is not clear whether it is suitable as an index used to identify an individual.

  Therefore, an object of the present invention is to provide a personal identification method capable of identifying an individual based on eye movement.

  The personal identification method of the present invention is a method executed by the personal identification device, and includes an eye movement acquisition step, a feature amount extraction step, and a personal identification step.

  The eye movement acquisition step acquires information related to the movement of the eyeball of the person to be identified. The feature amount extraction step extracts a representative feature amount including one or more feature amounts based on the movement of the eyeball. In the individual identification step, the person to be identified is identified based on the representative feature amount.

  According to the personal identification method of the present invention, an individual can be identified based on eye movement.

The figure explaining microsaccade. The block diagram which shows the structure of the personal identification device of 1st embodiment. The flowchart which shows operation | movement of the personal identification device of 1st embodiment. The figure explaining the feature-value of microsaccade. The figure showing the change of the magnitude | size of a pupil. The block diagram which shows the structure of the personal identification device of 2nd embodiment. The flowchart which shows operation | movement of the personal identification device of 2nd embodiment. The figure which shows the estimation precision by a 2nd personal identification model. The figure which shows the person acceptance rate by a 1st personal identification model. The figure which shows the other person rejection rate by a 1st personal identification model. The figure which shows the estimation precision by the 1st personal identification model on various conditions.

  Embodiments of the present invention will be described below. In the following description and drawings, components having the same function and steps performing the same process are denoted by the same reference numerals, and the description thereof will be omitted when the description is repeated.

[First embodiment]
Hereinafter, the personal identification device of the first embodiment will be described. The personal identification device of this embodiment identifies an individual based on minute jumping eye movements (hereinafter also referred to as microsaccades) that appear in the movement of the human eyeball. First, microsaccade will be described. When a person is gazing at a certain point, the eyeball does not stop moving completely, but three types of eye movement called fixation and tremor (drift, trend), trema, Micro saccade (may be called flick). Drift is a small smooth movement, trema is a very small high-frequency vibration, and microsaccade is a small jumping movement. FIG. 1 is a diagram illustrating a microsaccade, and is a graph showing an example of eye movement in a gaze state with the horizontal axis representing time [seconds] and the vertical axis representing a viewing angle [degree]. Microsaccades 101, 102, and 103, which are examples of microsaccades, are shown with bold lines. As shown in FIG. 1, the microsaccade is a movement of the eyeball that appears (involuntarily) regardless of the individual's intention once in 1 to 2 seconds in a state where a single point is being watched. It is a small jumping movement. Microsaccades can be obtained from either the horizontal or vertical component of motion. In this embodiment, based on the property that the microsaccade is deflected in the horizontal direction, only the horizontal component is used for simplicity. However, the microsaccade direction component that can be used in the present invention is not limited to the horizontal direction. . The “horizontal direction” is not limited to a direction parallel to the ground, but is a horizontal direction with respect to the face (an eyeball arrangement direction, which may be referred to as a horizontal direction or a width direction) or will be described later. This is a concept including a direction defined as a horizontal direction in the eye movement acquisition unit 11.

  Hereinafter, the configuration and operation of the personal identification device according to the present embodiment will be described with reference to FIGS. FIG. 2 is a block diagram showing the configuration of the personal identification device 10 of this embodiment. FIG. 3 is a flowchart showing the operation of the personal identification device 10 of the present embodiment. As shown in FIG. 2, the personal identification device 10 of the present embodiment includes an eye movement acquisition unit 11, a feature amount extraction unit 12, a personal identification unit 13, and a model storage unit 14. Information stored in the model storage unit 14 may be stored in an external device or the like, and the model storage unit 14 may be omitted as appropriate. Therefore, the model storage unit 14 is represented by a broken line.

<Eyeball movement acquisition unit 11>
The eye movement acquisition unit 11 acquires information on the movement of the eyeball of the identification target person 9 (information on the temporal change of the eyeball position) in the first time section that is a predetermined time section (S11). The eye movement acquisition unit 11 outputs information about the acquired eye movement to the feature amount extraction unit 12. In the present embodiment, the feature amount based on the movement of the eyeball includes a feature amount related to microsaccade and a feature amount related to blinking which will be described later. The length is set so that one or more microsaccades and blinks are included in the predetermined time interval (first time interval). The length of the first time interval is preferably about several seconds to several tens of seconds.

  For example, the identification target person 9 is allowed to gaze at a certain point, and the movement of the eyeball at that time is imaged with an infrared camera. The eye movement acquisition unit 11 can acquire the time series of the positions of the eyeballs for each frame (for example, at a sampling interval of 1000 Hz) as information related to the movement of the eyeballs by performing image processing on the imaging results. In this case, the eye movement acquisition unit 11 may be realized by an imaging device (infrared camera) and a computer that executes an image processing algorithm, or input from the imaging device using the imaging device (infrared camera) as an external device. It may be realized by a computer or the like that executes an algorithm for image processing of an image. Further, the eye movement acquisition unit 11 may measure the movement of the eye using an electric potential measurement method using electrodes. In this case, the eye movement acquisition unit 11 may be realized by a measurement device (including electrodes) and a computer that executes an algorithm for calculating the position of the eyeball based on the potential measured by the measurement device. The external device may be realized by a computer that executes an algorithm for calculating the position of the eyeball based on the potential input from the measurement device. Note that the eye movement acquisition unit 11 may acquire information regarding the movement of both the left and right eyeballs, or may acquire only information regarding the movement of either one of the eyeballs. In the present embodiment, the eye movement acquisition unit 11 acquires only information related to the movement of one eyeball.

  Based on the property that the microsaccade is deflected in the horizontal direction, in the present embodiment, only the component in the horizontal direction is used in the acquired information on the movement of the eyeball.

<Feature Extraction Unit 12>
The feature amount extraction unit 12 extracts a representative feature amount including one or more feature amounts based on the movement of the eyeball (S12). For example, the feature amount extraction unit 12 receives information related to the movement of the eyeball corresponding to the first time interval, detects microsaccades and blinks in the first time interval, and calculates feature amounts related to the microsaccades or blinks. One or more representative feature values including one or more are extracted (S12). The feature amount extraction unit 12 outputs the extracted representative feature amount to the personal identification unit 13. Note that the feature amount extraction unit 12 may detect only microsaccades in the first time interval, and may omit detection of blinks. In this case, the feature quantity extraction unit 12 extracts a representative feature quantity including one or more feature quantities of the microsaccade.

  For example, the feature amount extraction unit 12 calculates a primary difference series for the time series of the position information of the eyeball, and sets the time when the absolute value of the primary difference series exceeds a predetermined threshold as the start time when the microsaccade occurred. May be detected. However, if the length of time that the absolute value of the primary difference series exceeds the predetermined threshold does not last longer than the predetermined value (usually about 3 ms), it is excluded from detection. The feature amount extraction unit 12 may use a moving average value in an appropriate range in calculating the first-order difference series, for example, when it is determined that the acquired position information of the eyeball includes a lot of noise. The threshold used for detection is preferably a value about 6 times the standard deviation of the difference series.

  For example, the feature amount extraction unit 12 may detect a time interval in which no pupil is detected (a time interval in which the position information of the eyeball has not been acquired correctly) as a time interval in which a blink occurs. However, since it is generally considered that the length of time at which blinks occur is 75 ms at the minimum, the feature amount extraction unit 12 determines that the length of the detected time interval is below a predetermined threshold (for example, 50 ms). It is desirable to exclude this from blinking as noise.

The representative feature amounts corresponding to the first time interval are the reference amplitude A, the maximum velocity V _max , the duration D _m , the overshoot amplitude A _o , which will be described later, for all microsaccades detected in the first time interval, The overshoot speed V _o , the rise time T _p , the damping rate λ, the damping coefficient ζ, the natural angular frequency ω _n , the number of occurrences R _m per unit time (for example, 1 second) of the microsaccade, etc. The representative values of these feature amounts are calculated, and the representative values are used in parallel in vector representation. When a plurality of microsaccades are detected in the first time interval, a representative value of each feature value obtained for each microsaccade is calculated, and the representative values are used in parallel in a vector representation. The representative value is, for example, an average value, a maximum value, a minimum value, a value corresponding to the first microsaccade, and it is particularly preferable to use the average value. The representative feature amount includes, in addition to the representative value of each feature amount described above, the number of occurrences R _b per second in the first time interval, and the representative value D _b of the blink duration in the first time interval. May be added as a vector element. The blink duration D _b is the length of the time interval in which the blink occurs. The representative feature amount is not limited to one including all the above-described feature amounts as elements, and may be configured using a part of them.

With reference to FIG. 4, the microsaccade reference amplitude A, maximum velocity V _max , duration D _m , overshoot amplitude A _o , overshoot velocity V _o , rise time T _p , and decay rate λ will be described. . FIG. 4 is a diagram for explaining the feature amount of the microsaccade.
(1) Reference amplitude A: A movement amount when the movement of the eyeball by the microsaccade converges.
(2) Maximum speed V _max : Maximum speed until reaching the reference amplitude A + overshoot amplitude A _o .
(3) Duration D _m : the length of the time interval in which microsaccade is occurring. The start time of the microsaccade is a time when the absolute value of the first-order difference series exceeds a predetermined threshold, and the end time of the microsaccade is the time when the first return to the reference amplitude A is reached after reaching the overshoot amplitude. .
(4) Overshoot amplitude A _o : The amount of the portion that exceeds (overshoots) the reference amplitude A by the microsaccade. The overshoot is a phenomenon in which the waveform protrudes beyond the reference amplitude A at the rising portion of the waveform, or the protruding waveform. In other words, the overshoot amplitude is the amount of the protruding portion.
(5) Overshoot speed V _o : The maximum speed when attempting to converge to the reference amplitude A from the reference amplitude A + the overshoot amplitude A _o .
(6) Rise time T _p : Time required to reach (rise) the reference amplitude A + the overshoot amplitude A _o . The reference amplitude A + overshoot amplitude A _o time to reach of the same value as the time it takes from the maximum velocity V _max reach speeds V _o overshoot.
(7) Attenuation rate λ: ratio of overshoot amplitude A _o to reference amplitude A It may be the ratio of overshoot speed V _o to maximum speed V _max ,

It is expressed.

The attenuation coefficient ζ and the natural angular frequency ω _n of the microsaccade are calculated by modeling the time series of the eyeball position in the time interval where the microsaccade is occurring as a step response of the position control system. The step response of the position control system is

It is expressed. Here, G (s) is a transfer coefficient, y (t) is a position, and y '(t) is a velocity. Based on this, the damping coefficient ζ and the natural angular frequency ω _n of the microsaccade are

It is expressed. Here, t is an index representing time, and s is a parameter (complex number) by Laplace transform. The natural angular frequency ω _n corresponds to an index representing the response speed of the microsaccade, and the damping coefficient ζ corresponds to an index representing the convergence of the microsaccade response.

The feature extraction unit 12 fits the microsaccade damping coefficient ζ, the natural angular frequency ω _n , and the reference amplitude A with the function y (t) of the position of the eyeball while the microsaccade is occurring. You may calculate by optimizing by the square method.

  Since the value of the attenuation coefficient ζ of the microsaccade tends to change depending on the left-right direction, the feature amount extraction unit 12 uses the representative value of the attenuation coefficient of the left-side microsaccade, the right-side microsac- tery. The representative value of the saccade attenuation coefficient may be calculated separately.

  As described above, the attenuation coefficient ζ of the microsaccade is an index corresponding to the convergence of the response when the microsaccade is viewed as a step response of the position control system. The convergence of the microsaccade response is affected by the degree of relative movement of the pupil relative to the iris, and the degree of relative movement of the pupil iris is influenced by the degree of tension of the ciliary body holding the lens. Conceivable. Ciliary muscles are known to harden and slow down with age, and it is thought that differences in ciliary movement properties are observed as differences in convergence of microsaccades among individuals. .

<Model storage unit 14>
The model storage unit 14 receives a representative feature quantity and a predetermined personal label as input, and first representative identification model that can estimate whether the representative feature quantity has been acquired from the identification target person of the predetermined personal label, and the representative feature quantity The second personal identification model that can estimate the personal label of the person to be identified corresponding to the representative feature is stored in advance.

  The first personal identification model and the second personal identification model can be created by learning the relationship between the representative feature amount acquired in advance for one or more people and the personal label by a machine learning method. That is, the first personal identification model and the second personal identification model are models that describe the correlation between the representative feature quantity including the feature quantity of the microsaccade and the personal label.

  First, a common part between the first personal identification model and the second personal identification model will be described. For example, a time series of eyeball positions of a person (identification target person) for learning is acquired, and representative feature amounts are extracted from the acquired time series of eyeball positions. The representative feature amount extracted here is the same as the representative feature amount extracted by the feature amount extraction unit 12. A similar feature amount is extracted from the identification target person for a plurality of time intervals, and a data set in which the extracted representative feature quantity is associated with the personal label of the identification target person is prepared as learning data. As learning data, data obtained by accumulating data sets obtained from a plurality of persons (that is, a plurality of individual labels) is used. Using this learning data, the machine learning method is used to learn the relationship between the representative feature amount and the personal label. For example, there is a support vector machine (hereinafter also referred to as SVM) as a machine learning method.

  In the first personal identification model, for all representative feature amounts included in the learning data, 1 is the predetermined personal label L to which the person to be identified is input, and 0 is not the predetermined personal label L. Two patterns of values (hereinafter referred to as identification labels) are assigned, and in a space corresponding to the dimension of the feature amount, a point group corresponding to the feature vector when the identification label is 1 and a point when the identification label is 0 A hyperplane that separates the point cloud corresponding to the feature vector is obtained. For example, a support vector machine that classifies two classes is used as a machine learning method. Thus, when a feature quantity whose identification label is unknown (a representative feature quantity obtained by the feature quantity extraction unit 12) is input to the obtained first personal identification model, an identification label corresponding to the feature quantity can be estimated. . That is, it can be estimated whether the unknown feature amount is acquired from the identification target person 9 of the predetermined personal label L or not.

  In the second personal identification model, the SVM is configured to handle personal labels corresponding to all representative feature amounts included in the learning data and classify them into a plurality of classes (classes corresponding to personal labels). Alternatively, other machine learning methods may be used as long as the machine learning method identifies and classifies into a plurality of classes (classes corresponding to individual labels).

<Personal identification unit 13>
The personal identification unit 13 identifies the person to be identified 9 based on the representative feature amount (S13). For example, the personal identification unit 13 inputs the representative feature amount extracted by the feature amount extraction unit 12 and a predetermined personal label into the first personal identification model stored in the model storage unit 14, thereby obtaining the representative feature amount. It is estimated whether or not a predetermined personal label has been acquired from the identification target person 9 (S13).

  Alternatively, the personal identification unit 13 estimates the personal label by inputting only the representative feature amount extracted by the feature amount extraction unit 12 to the second personal identification model stored in the model storage unit 14 (S13). It can be specified in advance whether the personal identification unit 13 uses the first personal identification model or the second personal identification model.

<Effect>
According to the personal identification device 10 of the present embodiment, the personal label of the person to be identified 9 can be estimated based on the feature amount of the microsaccade and the like with the above-described configuration. The camera measurement of eye movement has less noise and the feature quantity of microsaccade can be estimated accurately with a single trial, so that the accuracy of personal identification is less susceptible to measurement errors. The camera measurement of the eyeball can be performed on a small scale without restraining the identification target person 9, and has an effect of reducing the psychological resistance of the identification target person 9 during personal identification. Microsaccades are minute eye movements that occur unconsciously, and it is difficult for the person to be identified 9 to intentionally control a feature quantity such as an attenuation coefficient. Therefore, it has an advantage that it is difficult to forge a sample as compared with personal authentication of fingerprints and irises.

[Second Embodiment]
Hereinafter, the personal identification device according to the second embodiment will be described focusing on differences from the first embodiment. In the present embodiment, an individual is identified based on both the change in the size of the pupil and the microsaccade. First, changes in the size of the pupil will be described with reference to FIG. When a person is gazing at a point, the size of the pupil is not constant but is changing. FIG. 5 is a diagram showing changes in pupil size in a gaze state, where the horizontal axis represents time [seconds] and the vertical axis represents pupil size [z-score].

  The size of the pupil is enlarged (mydriasis) by the pupil dilator that is controlled by the sympathetic nervous system, and contracted (miosis) by the pupil sphincter that is controlled by the parasympathetic nervous system. In FIG. 5, the double line portion represents the miosis, and the broken line portion represents the mydriasis. Changes in the size of the pupil are mainly classified into three types: light reflection, convergence reflection, and emotional changes. The light reflection is a reaction in which the size of the pupil changes in order to control the amount of light incident on the retina. A miosis occurs for strong light, and a mydriasis occurs in a dark place. Convergence reflex is a reaction in which the pupil diameter changes with the movement of both eyes inward or outward (convergence movement) when focusing. Arise. The emotional change is a reaction that occurs to the external stress regardless of any of the above. When my sympathetic nerve is dominant due to anger, surprise, or active activity, mydriasis occurs and relaxes. When the parasympathetic nerve dominates, miosis occurs.

  The configuration and operation of the personal identification device according to this embodiment will be described below with reference to FIGS. FIG. 6 is a block diagram showing the configuration of the personal identification device 20 of this embodiment. FIG. 7 is a flowchart showing the operation of the personal identification device 20 of the present embodiment. As shown in FIG. 6, the personal identification device 20 of the present embodiment includes a pupil information acquisition unit 21, an eye movement acquisition unit 11, a feature amount extraction unit 22, a personal identification unit 23, and a model storage unit 24. The operation of the eye movement acquisition unit 11 is the same as that in the first embodiment. Since the model storage unit 24 can be omitted for the same reason as in the first embodiment, it is represented by a broken line.

<Pupil information acquisition unit 21>
The pupil information acquisition unit 21 acquires information related to the temporal change in the size of the pupil of the identification target person 9 in the first time interval which is a predetermined time interval (S21). The pupil information acquisition unit 21 outputs information regarding the acquired change in the size of the pupil over time to the feature amount extraction unit 22. For example, when the pupil diameter (pupil radius) is used as the pupil size, the pupil diameter can be measured by an image processing method using an infrared camera. It is assumed that the length is set so that one or more miosis and mydriasis are included in the predetermined time interval (first time interval). The first time interval is preferably about several seconds to several tens of seconds.

  For example, it is assumed that the identification target person 9 gazes at a certain point, and the pupil at that time is imaged by an infrared camera. The pupil information acquisition unit 21 can acquire a time series of pupil sizes for each frame (for example, a sampling interval of 1000 Hz) by performing image processing on the imaging result. For example, the pupil information acquisition unit 21 can fit a circle to the pupil of an image obtained by photographing the pupil and use the radius of the fitted circle as the pupil diameter. Since the pupil diameter varies minutely, it is preferable that the pupil information acquisition unit 21 uses the value of the pupil diameter smoothed (smoothed) for each predetermined time interval. Here, the size of the pupil shown in FIG. 5 is expressed using z-score, where the average of all the pupil diameter data acquired at each time is 0 and the standard deviation is 1. Smoothed at intervals of 150ms. However, the pupil diameter acquired by the pupil information acquisition unit 21 may not be a z-score, may be a pupil diameter value itself, or a value corresponding to the size of the pupil, such as the area or diameter of the pupil. Anything is fine. Even when the area or diameter of the pupil is used, a section in which the area or diameter of the pupil increases with the passage of time corresponds to mydriasis, and a section in which the area or diameter of the pupil decreases with the passage of time corresponds to miosis. That is, a section in which the size of the pupil increases with the passage of time corresponds to the mydriasis, and a section in which the size of the pupil decreases with the passage of time corresponds to the miosis.

  In general, the amount of change in the size of the pupil due to the reflection of light is several times larger than the amount of change due to emotion, which is a major factor for the entire amount of change in pupil size. In order to suppress changes due to light reflection, it is desirable that the person 9 to be identified when acquiring the pupil diameter is in a state where the illuminance of the ambient light does not change abruptly.

  Note that the pupil information acquisition unit 21 may acquire information related to temporal changes in the sizes of both the left and right pupils, or may acquire only information related to temporal changes in the size of one of the pupils. In the present embodiment, it is assumed that the pupil information acquisition unit 21 acquires only information related to the temporal change in the size of one pupil.

<Feature Extraction Unit 22>
The feature amount extraction unit 22 extracts one or more feature amounts based on eyeball movements described in the first embodiment and one or more feature amounts based on temporal changes in pupil size (S22). ). For example, the feature amount extraction unit 22 receives information on eye movements corresponding to the first time interval and a time series of pupil sizes, and performs microsaccade, blink, miosis, and mydriasis in the first time interval. A representative feature amount including one or more feature amounts related to microsaccade, blink, miosis, and mydriasis is extracted (S22). The feature amount extraction unit 22 outputs the extracted representative feature amount to the personal identification unit 23. The feature quantity extraction unit 22 performs microsaccade and blink detection in the same manner as the feature quantity extraction unit 12.

The feature amount extraction unit 22 detects the time at which the miosis starts (hereinafter referred to as the miosis start point) by extracting the maximum point from the time series of the pupil diameter. The time at which the miosis ends (hereinafter referred to as the miosis end point) is the earlier of the point at which the mydriasis start for the first time after the start of miosis or the point at which the blinks start for the first time after the start of miosis. Amplitude A _c of miosis is the difference in the pupil diameter from the miosis start point to miosis end point. The miosis duration D _c is the time difference from the miosis start point to the miosis end point. The average speed V _{c of} miosis is (amplitude A _c ) / (duration D _c ).

The feature quantity extraction unit 22 detects the time at which the mydriatic starts (hereinafter, mydriatic start point) by extracting a local minimum point from the time series of the pupil diameter. The time at which the mydriatic ends (hereinafter referred to as the mydriatic end point) is the earlier of the time when the miosis starts for the first time after the start of mydriasis or the time when the blink starts for the first time after the start of mydriasis. Amplitude A _d mydriatic is the difference between the pupil diameter from mydriasis start point to mydriasis end point. The mydriatic duration D _d is the time difference from the mydriatic start point to the mydriatic end point. The average speed V _{d of the} mydriasis is (amplitude A _d ) / (duration D _d ).

  In order to prevent erroneous detection due to noise, when the duration of the miosis is a predetermined threshold (for example, 10 ms) or less, or when the amplitude of the miosis is less than the predetermined threshold, the feature amount extraction unit 22 performs the miosis. Should be excluded from detection. Similarly, if the duration of the mydriasis is a predetermined threshold (for example, 10 ms) or less, or if the amplitude of the mydriasis is a predetermined threshold or less, the feature amount extraction unit 22 excludes the mydriatic from detection. Good.

The representative features corresponding to the first time interval are the reference amplitude A, the maximum velocity V _max , the duration D _m , the overshoot amplitude A _o , and the overshoot amplitude of all the microsaccades detected in the first time interval. V _o , rise time T _p , damping rate λ, damping coefficient ζ and natural angular frequency ω _n , number of occurrences of microsaccade per unit time R _m in the first time interval, and detected in the first time interval The amplitude A _c of all the miosis, the duration D _{c of} the miosis, the average velocity V _{c of the} miosis, the amplitude A _d of all the mydriasis detected in the first time interval, and the duration D of the mydriasis _d and average mydriatic velocity V _d are calculated respectively, and representative values of the above-described feature values are obtained for all microsaccades, miosis, and mydriasis generated in the first time interval, and the above-described feature values are obtained. Parallel each representative value in vector representation Were the ones used. The representative value is, for example, a value corresponding to an average value, a maximum value, or a minimum value, and it is particularly preferable to use an average value. The representative feature amount includes, in addition to the representative value of each feature amount described above, the number of occurrences R _b per second in the first time interval, and the representative value D _b of the blink duration in the first time interval. Alternatively, the number of occurrences of miosis per second R _c in the first time interval and the number of occurrences R _d of mydriatic per second in the first time interval may be added as vector elements. The representative feature amount is not limited to one including all the above-described feature amounts as elements, and may be configured using a part of them.

<Personal identification unit 23>
The personal identification unit 23 inputs the representative feature amount extracted by the feature amount extraction unit 22 and a predetermined personal label into the first personal identification model stored in the model storage unit 24, whereby the representative feature amount is predetermined. It is estimated whether or not the personal label has been acquired from the person to be identified 9 (S23).

  Alternatively, the personal identification unit 23 estimates the personal label by inputting only the representative feature amount extracted by the feature amount extraction unit 22 to the second personal identification model stored in the model storage unit 24 (S23). It can be specified in advance whether the personal identification unit 23 uses the first personal identification model or the second personal identification model.

<Model storage unit 24>
The model storage unit 24 is the same as the model storage unit 14. In other words, the first personal identification model that estimates whether or not the representative feature quantity and the predetermined personal label are acquired from the identification target person 9 of the predetermined personal label, and only the representative feature quantity are input. The second personal identification model that estimates the personal label of the person to be identified 9 who has acquired the representative feature amount is recorded in advance. However, the difference is that the representative feature quantity input to the model includes the feature quantity of miosis or mydriasis.

<Effect>
According to the personal identification device 20 of the present embodiment, in addition to the same effects as those of the first embodiment, by adding a parameter related to the temporal change in the size of the pupil to the representative feature amount, further accurate personal identification can be performed. realizable.

<Other variations>
The present invention is not limited to the above-described embodiment, and can be appropriately changed without departing from the spirit of the present invention. In addition, the processing described in the above embodiment may be executed not only in time series according to the order of description but also in parallel or individually as required by the processing capability of the apparatus that executes the processing. .

  Examples of the present invention and the results thereof will be described below with reference to FIGS. In this example, eye movements and pupil diameter changes were recorded at a sampling rate of 1000 Hz using a near-infrared camera for 23 subjects. Each person recorded 15 times, and each measurement was 90 seconds long.

  In order to verify the difference in estimation accuracy due to the length of measurement time, four conditions obtained by cutting out some time sections (10 seconds, 20 seconds, 40 seconds, 80 seconds) of each sample (90 seconds) were individually The following examination was conducted.

  Blink detection was based on eye movement data, and those with a duration of 30 ms or less were excluded. The velocity dimension of eye movement data was used for microsaccade detection, and the threshold was set to 8 times the standard deviation. In the detection of mydriasis and miosis, time series data of pupil diameter changes is used, and after smoothing in a 150 ms time window, the minimum and maximum points are extracted, and the reduction is considered to be 5 ms or less in duration. Pupil and mydriatic were excluded.

  The representative features of each sample include microsaccade frequency, amplitude, maximum speed, duration, overshoot speed, leftward damping coefficient, rightward damping coefficient, natural angular frequency, blink frequency・ Calculate duration, mydriatic frequency, amplitude, average velocity, duration, miosis frequency, amplitude, average velocity, duration, and use the average value of each sample in time It was.

  FIG. 8 shows the estimation accuracy according to the second individual identification model (a model for identifying the individual label of the person to be identified with the representative feature quantity as an input). Specifically, N individual (from 2 to 23) personal labels and representative feature values of 14 samples per N person are input, and using SVM, representative feature values and N different personal labels A learning model that associates Based on the learning model, the personal label was estimated from the representative feature quantity of one sample per person that was not used for learning, and it was verified whether it matched the actual personal label. Similar learning and estimation were repeated 25 times with random combinations of people, and the average estimation accuracy was obtained. As a result of estimation, if the target number is 7 or less, the personal label is correctly over 90% if the sample length is 20 seconds, and if the target number is 23, the personal label is correctly over 90% if the sample length is 80 seconds. I was able to estimate.

  FIG. 9 and FIG. 10 show the person according to the first personal identification model (a model for identifying whether or not the representative feature quantity is acquired from the identification target person of the predetermined personal label, with the representative feature quantity and the predetermined personal label as inputs). Shows the acceptance rate and other person rejection rate. Specifically, a value indicating whether or not N people (2 to 23 people) have a predetermined personal label (hereinafter, personal label L) (1 if personal label L, 1 if not personal label L) 0. Hereafter, an identification label) and representative features of 14 samples per N people were input, and a learning model was created that associates the representative features with the identification label using SVM. Based on the learning model, the identification label was estimated from the representative feature quantity of one sample per person that was not used for learning, and it was verified whether it matched the actual identification label. Similar learning and estimation were repeated by changing the personal label L in N ways, and further repeating the combination of people at random five times to obtain the average estimation accuracy. At this time, the probability of correctly estimating the identification label as 1 when the actual identification label is 1 is the person acceptance rate, and the probability of erroneously estimating the identification label as 0 when the actual identification label is 1 is The rejection rate, the probability of correctly estimating the identification label as 0 when the actual identification label is 0, the other person rejection rate, the probability of erroneously estimating the identification label as 1 when the actual identification label is 0, etc. It can be said that the acceptance rate. As a result of estimation, the acceptance rate is over 90% for a sample length of 80 seconds when the target number is 20 or less, and over 50% for a sample length of 10 seconds even when the target number is 23. (Fig. 9). In addition, the rejection rate of others was over 99% regardless of the sample length even when the target number was 23 (FIG. 10). According to this result, it can be said that the probability of erroneously authenticating a sample other than the principal as the principal is very low.

  FIG. 11 is a diagram illustrating the estimation accuracy based on the first individual identification model under various conditions. FIG. 11 shows the result of verifying the same estimation as described above by the first individual identification model by changing the elements included in the representative feature amount. Specifically, when the basic features (occurrence frequency, amplitude, maximum speed, duration) of microsaccades are used (bar graph with thin hatching), all the features of microsaccades (occurrence frequency, When using amplitude (maximum speed, duration, overshoot speed, leftward damping coefficient, rightward damping coefficient, natural angular frequency) (bar graph with dark hatching), all features ( Microsaccade frequency, amplitude, maximum speed, duration, overshoot speed, left damping coefficient, right damping coefficient, natural angular frequency, blink frequency, duration, mydriatic frequency Amplitude / average speed / duration, miosis occurrence frequency / amplitude / average speed / duration) (black bar graph) results are shown. However, the sample length is 80 seconds and N is 23 people. As a result of the estimation, if only the basic features of microsaccade are used, the acceptance rate is about 57%, and if all the features of microsaccade are used, the acceptance rate is about 86%, and all features When using, the acceptance rate was about 90%, and in all cases the rejection rate was over 99%. According to this result, it can be said that the use of the microsaccade overshoot, the damping coefficient, and the natural angular frequency, which are features of the present invention, as the representative feature amount greatly contributes to the improvement of the estimation accuracy.

<Supplementary note>
The apparatus of the present invention includes, for example, a single hardware entity as an input unit to which a keyboard or the like can be connected, an output unit to which a liquid crystal display or the like can be connected, and a communication device (for example, a communication cable) capable of communicating outside the hardware entity. Can be connected to a communication unit, a CPU (Central Processing Unit, may include a cache memory or a register), a RAM or ROM that is a memory, an external storage device that is a hard disk, and an input unit, an output unit, or a communication unit thereof , A CPU, a RAM, a ROM, and a bus connected so that data can be exchanged between the external storage devices. If necessary, the hardware entity may be provided with a device (drive) that can read and write a recording medium such as a CD-ROM. A physical entity having such hardware resources includes a general-purpose computer.

  The external storage device of the hardware entity stores a program necessary for realizing the above functions and data necessary for processing the program (not limited to the external storage device, for example, reading a program) It may be stored in a ROM that is a dedicated storage device). Data obtained by the processing of these programs is appropriately stored in a RAM or an external storage device.

  In the hardware entity, each program stored in an external storage device (or ROM or the like) and data necessary for processing each program are read into a memory as necessary, and are interpreted and executed by a CPU as appropriate. . As a result, the CPU realizes a predetermined function (respective component requirements expressed as the above-described unit, unit, etc.).

  As described above, when the processing functions in the hardware entity (the apparatus of the present invention) described in the above embodiments are realized by a computer, the processing contents of the functions that the hardware entity should have are described by a program. Then, by executing this program on a computer, the processing functions in the hardware entity are realized on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used. Specifically, for example, as a magnetic recording device, a hard disk device, a flexible disk, a magnetic tape or the like, and as an optical disk, a DVD (Digital Versatile Disc), a DVD-RAM (Random Access Memory), a CD-ROM (Compact Disc Read Only). Memory), CD-R (Recordable) / RW (ReWritable), etc., magneto-optical recording medium, MO (Magneto-Optical disc), etc., semiconductor memory, EEP-ROM (Electronically Erasable and Programmable-Read Only Memory), etc. Can be used.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Furthermore, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage device. When executing the process, the computer reads a program stored in its own recording medium and executes a process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program in this embodiment includes information that is used for processing by an electronic computer and that conforms to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  In this embodiment, a hardware entity is configured by executing a predetermined program on a computer. However, at least a part of these processing contents may be realized by hardware.

Claims (7)

A personal identification method executed by a personal identification device,
An eye movement acquisition step for acquiring information on the movement of the eyeball of the identification subject in a resting or gaze state ;
Based on the movement of the eye, a feature amount extraction step to extract the representative value as a representative feature quantity is a statistical quantity of feature parameters for each micro saccade in a predetermined time interval,
A personal identification method including a personal identification step of identifying the identification target person based on the representative feature amount. The personal identification method according to claim 1 ,
As the type of feature amount related to the microsaccade, the reference amplitude, maximum speed, duration, overshoot amplitude, overshoot speed, rise time, damping rate, damping coefficient, natural angular frequency, unit of the microsaccade A personal identification method including at least one of occurrences per hour.   The personal identification method according to claim 1 or 2,
  The personal identification step includes
  Based on the first personal identification model learned to output the identification result as to whether or not the representative feature is acquired from the identification target person of the predetermined personal label, using the representative feature as an input It is identified whether or not the representative feature amount extracted in the amount extraction step is obtained from an identification target person of a predetermined personal label.
Personal identification method.   The personal identification method according to claim 1 or 2,
  The personal identification step includes
  Corresponding to the representative feature amount extracted in the feature amount extraction step based on the second personal identification model learned to output the estimation result of the personal label corresponding to the representative feature amount with the representative feature amount as an input Estimate personal labels
Personal identification method. The personal identification method according to any one of claims 1 to 4,
Further including a pupil information acquisition step of acquiring information related to a temporal change in the size of the pupil of the identification target;
The feature amount extraction step includes:
A personal identification method for extracting a representative feature amount including at least one feature amount based on a movement of the eyeball and including at least one feature amount based on a temporal change in the size of the pupil. An eye movement acquisition unit for acquiring information on the movement of the eyeball of the identification target person in a resting or gaze state ;
Based on the movement of the eye, a feature amount extraction unit to extract a representative value as the representative feature quantity is a statistical quantity of feature parameters for each micro saccade in a predetermined time interval,
A personal identification device including a personal identification unit for identifying the identification target person based on the representative feature amount.   The program for making a computer perform each step of the personal identification method described in any one of Claim 1 to 5.

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