WO2021101192A1 - Procédé et appareil d'authentification d'un utilisateur - Google Patents

Procédé et appareil d'authentification d'un utilisateur Download PDF

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Publication number
WO2021101192A1
WO2021101192A1 PCT/KR2020/016098 KR2020016098W WO2021101192A1 WO 2021101192 A1 WO2021101192 A1 WO 2021101192A1 KR 2020016098 W KR2020016098 W KR 2020016098W WO 2021101192 A1 WO2021101192 A1 WO 2021101192A1
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WIPO (PCT)
Prior art keywords
data
user
finger
fingerprint
registered
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PCT/KR2020/016098
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English (en)
Inventor
Vipul Gupta
Ankur Agrawal
Rahul Agrawal
Prashant SHARMA
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Samsung Electronics Co., Ltd.
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Application filed by Samsung Electronics Co., Ltd. filed Critical Samsung Electronics Co., Ltd.
Publication of WO2021101192A1 publication Critical patent/WO2021101192A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G06F21/32—User authentication using biometric data, e.g. fingerprints, iris scans or voiceprints
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/30—Authentication, i.e. establishing the identity or authorisation of security principals
    • G06F21/31—User authentication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
    • 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
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
    • G06V40/1318—Sensors therefor using electro-optical elements or layers, e.g. electroluminescent sensing
    • 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
    • G06V40/20—Movements or behaviour, e.g. gesture recognition
    • G06V40/28—Recognition of hand or arm movements, e.g. recognition of deaf sign language
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2203/00—Indexing scheme relating to G06F3/00 - G06F3/048
    • G06F2203/01—Indexing scheme relating to G06F3/01
    • G06F2203/011—Emotion or mood input determined on the basis of sensed human body parameters such as pulse, heart rate or beat, temperature of skin, facial expressions, iris, voice pitch, brain activity patterns
    • 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
    • G06V40/15—Biometric patterns based on physiological signals, e.g. heartbeat, blood flow
    • 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
    • G06V40/40—Spoof detection, e.g. liveness detection

Definitions

  • the present disclosure relates to authenticating a user of a device. More particularly, the disclosure relates to a method and a device for authenticating a user with biometric data and mental-awareness data.
  • biometrics e.g., fingerprint, iris, facial recognition or the like
  • biometrics can detect an existence of any user with its physical uniqueness but it is unable to detect a mental awareness of the user while detecting the biometrics. This could lead to a misuse of biometrics by any person while the user is mentally unaware (e.g., sleeping state, unconscious state or the like).
  • FIG. 1A is an example scenario in which an electronic device 100 authenticates a user, according to the prior art.
  • a finger print scanning is used as password to unlock the electronic device 100 for many afterward-activities (e.g. financial transactions activity, accessing a social media, or the like).
  • afterward-activities e.g. financial transactions activity, accessing a social media, or the like.
  • any person can fraudulently unlock his/her electronic device 100 without his knowledge.
  • the person with fraudulent purposes can access device owner's personal data and make various activities without the user's knowledge.
  • the method includes obtaining, at a fingerprint scanner, fingerprint data and motion data of the user; obtaining finger pattern data and finger velocity data based on the motion data of the user; comparing the fingerprint data, the finger pattern data, and the finger velocity data with registered fingerprint data, registered finger pattern data, and registered finger velocity data, respectively; and determining whether to authenticate the user based on the comparison.
  • the disclosure provides a method of detecting not only the biometrics of a user but mental awareness(consciousness) of the user while detecting the biometrics. Thus, the disclosure prevent any unintended use of the eletronic device by any third party.
  • FIG. 1A is an example scenario in which an electronic device authenticates a user
  • FIG. 1B is an example scenario in which the electronic device authenticates a user, according to an embodiment
  • FIG. 2A illustrates a change of capacitance value in a fingerprint scanner, according to an embodiment
  • FIG. 2B illustrates exemplary capacitance values of cells touched by a user finger on a fingerprint scanner, according to an embodiment
  • FIG. 2C illustrates an optical type fingerprint scanner, according to an embodiment
  • FIG. 2D illustrates a ultrasonic type fingerprint scanner 2400, according to an embodiment
  • FIG. 3 illustrates an example scenario in which a fingerprint signature is registered
  • FIG. 4 illustrates unintentional touch data obtained based on extrapolation checkpoints, according to an embodiment
  • FIG. 5 illustrates the electronic device for authenticating the user, according to an embodiment.
  • FIG. 6 is a flow diagram for registering authentication data, , according to an embodiment
  • FIG. 7 illustrates a flow diagram for authenticating the user of the electronic device, according to an embodiment
  • FIGS. 8A, 8B, 8C, and 8D are example illustrations of an encryption process, according to an embodiment
  • FIG. 9 is an example illustration of an acceleration pattern during the registration of sensed data, according to an embodiment.
  • FIG. 10, FIG. 11 and FIG. 12 illustrate example scenarios in which the electronic device authenticates the user using various supplementary information, according to an embodiment.
  • embodiments herein disclose a method for authenticating a user of an electronic device.
  • the method includes obtaining, at a fingerprint scanner, fingerprint data and motion data of the user; obtaining finger pattern data and finger velocity data based on the motion data of the user; comparing the fingerprint data, the finger pattern data, and the finger velocity data with registered fingerprint data, registered finger pattern data, and registered finger velocity data, respectively; and determining whether to authenticate the user based on the comparison.
  • the obtaining of the finger pattern data and the finger velocity data includes obtaining, at a fingerprint scanner, first fingerprint data and first motion data of the user, wherein the first motion data comprises first finger pattern data and the first finger velocity data; and registering the first fingerprint data, the first pattern data, and the first finger velocity data of the user as the registered fingerprint data, the registered finger pattern data, and the registered finger velocity data, respectively.
  • the method further includes obtaining a capacitance value of extrapolation cells among cells of the fingerprint scanner based on measuring changes of capacitance values in cells which are unintentionally touched by the user.
  • the extrapolation cells are cells unintentionally touched for authentication and untouched during the registration by the user.
  • the method further includes obtaining finger acceleration data with the finger velocity data by measuring changes of capacitance values per a predetermined period of time in each cell of the fingerprint scanner, and wherein the comparing the fingerprint data, the finger pattern data, and the finger velocity data comprises comparing the finger acceleration data with registered finger acceleration data.
  • the obtaining of the finger acceleration data includes obtaining the finger acceleration data by measuring a degree of the changes of capacitance values per the predetermined period of time in each cell of the fingerprint scanner.
  • the obtaining secondary data of the user wherein the secondary data includes at least one of heart rate of the user, face pattern of the user, iris data of the user, grip pressure of the user, and body temperature of the user, and wherein the comparing the fingerprint data, the finger pattern data, and the finger velocity data includes comparing the secondary data of the user with a threshold value of the secondary data of the user.
  • the grip pressure of the user indicates pressure data when the user grips edges of the device while inputting user's fingerprint on the fingerprint scanner.
  • the obtaining of the secondary data of the user includes obtaining the heart rate of the user or the body temperature of the user with a wearable device wirelessly coupled to the device.
  • the finger pattern data includes a set of fingerprint data with a direction within a predetermined range and a pressure within a predetermined range.
  • the fingerprint scanner includes a ultrasonic fingerprint scanner configured to detect changes of ultrasonic pulses with respect to ridges and valleys of fingers of the user.
  • the fingerprint scanner includes an optical fingerprint scanner configured to detect changes of optical values with respect to ridges and valleys of fingers of the user.
  • an apparatus for authenticating a user includes a fingerprint scanner; a processor configured to obtain fingerprint data and motion data of the user with the fingerprint scanner, obtain finger pattern data and finger velocity data based on the motion data of the user, compare the fingerprint data, the finger pattern data, and the finger velocity data with registered fingerprint data, registered finger pattern data, and registered finger velocity data, respectively, and determine whether to authenticate the user based on the comparison; and a memory which stores the registered fingerprint data, the registered finger pattern data, and the registered finger velocity data.
  • any terms used herein such as but not limited to “includes,” “comprises,” “has,” “consists,” and grammatical variants thereof do NOT specify an exact limitation or restriction and certainly do NOT exclude the possible addition of one or more features or elements, unless otherwise stated, and furthermore must NOT be taken to exclude the possible removal of one or more of the listed features and elements, unless otherwise stated with the limiting language “MUST comprise” or “NEEDS TO include.”
  • inventions herein achieve a method for authenticating a user of an electronic device.
  • the method includes obtaining, by the electronic device, an authentication criteria.
  • the authentication criteria includes at least one sensor data of the user and at least one of a motion data and an unintentional touch data obtained from a display of the electronic device.
  • the method includes determining, by the electronic device, whether the obtained authentication criteria matches with a predefined authentication criteria, wherein the predefined authentication criteria comprises at least one predefined sensor data of the user and at least one of a predefined motion data and a predefined unintentional touch data obtained from the display of the electronic device.
  • the method includes performing, by the electronic device, one of: in response to the obtained authentication criteria matches with the predefined authentication criteria, authenticating the user of the electronic device, and in response to the obtained authentication criteria does not match with the predefined authentication criteria, denying the authentication of the user of the electronic device.
  • the method can be used to enhance a biometric security by using the fingerprint data of the user, the motion data associated with the fingerprint signature of the user, and the unintentional touch data associated with the fingerprint signature of the user.
  • the method can be used to enhance the biometric security without depends on which scanner the user touches on a 2D array.
  • the method can be used to encrypt the continuous motion of the complete portion of the finger touching the scanner in the form of changing values of each cell with respect to time using a current value of the fingerprint information and depth of the fingerprint information. This results in enhancing the biometric security.
  • the method can be used to authenticate the user both physically by means of biometric scanners and mentally by finger signature on the scanner by utilizing complete fingerprint information and encrypt a range of a parameter (such as current value, depth etc.) for each scanners present in the 2D array.
  • the method can be used to enhance the biometric security without requiring any display of path on a display.
  • FIGS. 2A through 12 embodiments are described in more detail.
  • FIG. 1B is an example scenario in which an electronic device 100 authenticates a user, according to an embodiment.
  • the electronic device 100 may be, for example, but not limited to a smart phone, a Personal Digital Assistant (PDA), a tablet computer, a laptop computer, a smart watch, a smart band, an immersive device, a virtual reality device, an Internet of Things (IoT) or the like.
  • PDA Personal Digital Assistant
  • IoT Internet of Things
  • the electronic device 100 is unlocked if the electronic device 100 satisfies an authentication criteria.
  • the authentication criteria may be satisfied if sensor data of the user and/or motion data and an unintentional touch data obtained from the display of the electronic device 100 meets a predetermined criteria.
  • the predetermined criteria may be determined by the previously registered sensor data and a degree of differences between the inputted sensor data and the previously registered sensor data.
  • the display may include a fingerprint scanner which includes a plurality of touch sensitive cells and is configured to obtain fingerprint of the user therewith.
  • the sensor data may include fingerprint data, iris data, grip pressure data, heart rate data, a facial recognition data or any other sensor-detected information.
  • the user may register his/her fingerprint, iris, grip pressure, heart rate, and/or face and store them in a memory 130 of the electronic device 100 as the above-noted previously registered sensor data.
  • the motion data is determined by a change of capacitance value in each portion of the display when the user provides a touch gesture including a signature input or an unlock pattern on the display.
  • the signature of the user may include unique touch pattern with unique direction changes by the user and with unique handwriting by the user.
  • each portion of the display may indicate each cell in the fingerprint scanner.
  • the touch gesture may be provided using a stylus pen.
  • the unintentional touch data is determined by an extrapolation checkpoint, when the touch gesture is inputted on the electronic device 100. The extrapolation checkpoints correspond to cells unintentionally touched during the authentication process but untouched during the registration process by the user.
  • the extrapolation checkpoints may help to avoid an unwanted authentication failure.
  • the unwanted authentication failure may occur when the cells corresponding to the extrapolation checkpoints which were untouched during the registration are touched during authentication.
  • the fingerprint data of the user is obtained using a fingerprint scanner (e.g., capacitive 2D array fingerprint scanner, optical type fingerprint scanner, ultrasonic type fingerprint scanner or the like).
  • the motion data and/or the unintentional touch data may be obtained by a fingerprint signature - a touch gesture.
  • the motion data is obtained by learning time variation between different touches in a pattern sequence of the touch gesture and whole time taken by the user in different attempts of registration to complete the signature.
  • the motion data is determined by a change of capacitance value - farad value - in each portion of the fingerprint scanner when the user provides the touch gesture including a fingerprint signature on the fingerprint scanner.
  • each portion may correspond to each cell of the fingerprint scanner sensing a touch including fingerprint.
  • the change of the farad value may represent continuous motion of a whole portion of a user input touching the fingerprint scanner in a predetermined time.
  • the fingerprint signature formed by the finger of the user on the fingerprint scanner may be used to detect the mental availability/awareness of the user.
  • the fingerprint scanner uses touch sensing arrays including capacitor circuits to collect touch data such as the fingerprint of the user.
  • capacitors can store electrical charge
  • the circuit arrangement of the fingerprint scanner connect them up to conductive plates on the surface of the scanner allows them to be used to track the details of the fingerprint.
  • the charge stored in the capacitor will be changed slightly when a finger's ridge is placed over the conductive plates, while an air gap (valley) will leave the charge at the capacitor relatively unchanged. Because of this characteristics, the capacitor in a cell under a ridge of a finger will have a greater capacitance value than the capacitor in a cell under a valley of the finger.
  • FIG. 2A illustrates a change of capacitance value in a fingerprint scanner, according to an embodiment.
  • a capacitive 2D array fingerprint scanner 2100 is shown.
  • the bank of capacitors are included in the capacitive 2D array fingerprint scanner.
  • Each capacitor in the bank of capacitors may be also called as a cell or a capacitor cell throughout the current description.
  • the 4x4 cells 2101 among the bank of capacitors represent farad values of each of the capacitor.
  • the farad value of the 4x4 cells 2101 differ each other based on ridges and valleys of a finger touched thereon.
  • the ridges are raised lines of epidermis (i.e., outer layer of skin) on the finger. These consist of 3 types: arch, loop & whorl.
  • the valleys are, on the other hand, a recessed portion of epidermis on the finger.
  • the FIG. 2A shows fingerprint sensing cells, in which the touched area is oval shaped.
  • the values on the fingerprint sensing cells are shown as farad values.
  • FIG. 2B illustrates exemplary capacitance values of cells touched by a user finger on a fingerprint scanner, according to an embodiment.
  • each of capacitance values of cells touched by the user finger 2201 may be encrypted for security purpose.
  • the touched cells in the fingerprint scanner 2200 have their own capacitance values.
  • FIG. 2C illustrates an optical type fingerprint scanner 2300, according to an embodiment.
  • Each of the ridges and valleys of a finger 2201 may reflect light emitted from the light source 2301 in a different way and thus, the detector 2303 may collect or detect the reflected light to form and recognize the fingerprint.
  • the motion of the finger over the fingerprint scanner can be validated to form the fingerprint signature of the user over a period of time. While registration of the fingerprint signature, each touch point on the optical type fingerprint scanner 2300 will reflect changes in optics due to motion of ridges and valleys of the finger 2201.
  • FIG. 2D illustrates a ultrasonic type fingerprint scanner 2400, according to an embodiment.
  • the ultrasonic type fingerprint scanner 2400 Each of the ridges and valleys of a finger 2201 may reflect ultrasonic pulses emitted from the ultrasonic transmitter 2401 in a different way and then the ultrasonic receiver 2403 may collect or receive the reflected ultrasonic pulses to form and recognize the fingerprint 2405.
  • the motion of the finger over the ultrasonic type fingerprint scanner 2400 can be validated to form the fingerprint signature of the user over a period of time. While registration of the fingerprint signature, each touch point on the ultrasonic type fingerprint scanner 2400 will reflect changes of ultrasonic pulse due to motion of ridges and valley of the finger 2201.
  • FIG. 3 illustrates an example scenario in which a fingerprint signature is registered.
  • the fingerprint signature is obtained and registered by sensing and storing a sequence of touches 3111, 3112, 3113, 3114, 3115 and 3116 in a predetermined pattern over a certain time period.
  • a combination of the pattern, direction, pressure of the finger, capacitance values and the time period between the starting touch 3111 and the final touch 3116 may be registered and stored in a memory of the electronic device including the fingerprint scanner 3100.
  • the fingerprint signature may be determined with an average values of the pattern, direction, pressure of the finger, capacitance values and the time period derived from several attempts of the same sequence of touches 3111, 3112, 3113, 3114, 3115 and 3116.
  • each of t1, t2, t3, t4, t5 and t6 a time point corresponding to each of the sequence of touches 3111, 3112, 3113, 3114, 3115 and 3116 .
  • Each of the time period ⁇ t1(t2-t1), ⁇ t2(t3-t2), ⁇ t3(t4-t3), ⁇ t4(t5-t4), and ⁇ t5(t6-t5) represents a time period between one of the sequence of touches and the next touch among the sequence of touches - i.e., ⁇ t1 represents a time period between the first touch 3111 and the second touch 3112, ⁇ t2 represents a time period between the second touch 3112 and the third touch 3113 and so forth.
  • the sequence of touches may not be discrete touches but continuous touch. In such case, the sequence of touches may be determined by a predetermined interval with respect to overall time taken from the first touch 3111 to the final touch 3116.
  • each of the sequence of touches covers a portion of grid(cell) in the fingerprint scanner 3100.
  • the capacitance value (farad value) is determined for each cell for encryption purpose.
  • the user's fingerprint signature pattern is drawn for a certain timeline on the display so that each of the sequence of touches can be evaluated on the basis of timeline from t1 to t6.
  • the capacitance value of cells for each touch among the sequence of touches 3111 to 3116 may be obtained at each of time points - t1, t2, t3, t4, t5 and t6, respectively.
  • the electronic device 100 can calculate changes in capacitance values of each cell involved in the movement from the touch 3111 to another touch 3112, from 3112 to 3113, from 3113 to 3114 from 3115 to 3116.
  • the changes of capacitance value with respect to the time period ⁇ t1 is used for encrypting the movement from the touch 3111 to the touch 3112.
  • the extrapolation checkpoint corresponds to a portion of the display or a cell of the fingerprint scanner unintentionally touched by the user, when the fingerprint signature is inputted on the electronic device 1000. In an example, whether a touch is unintentional is determined by obtaining an extrapolation checkpoint corresponding to the fingerprint signature.
  • the electronic device 100 is configured to determine whether the obtained data at a fingerprint scanner satisfies an predefined authentication criteria.
  • the predefined authentication criteria is obtained when the fingerprint data is registered for biometric authentication.
  • the predefined authentication criteria includes predetermined fingerprint data of the user and predetermined motion data of the user.
  • the predefined authentication criteria may include unintentional touch data obtained from the display of the electronic device.
  • the unintentional touch data may include changes of capacitance values in cells - extrapolation cells - which are unintentionally touched by the user. For example, the extrapolation cells may be determined when a cell is touched at a certain attempt for registration but is not touched at another attempt for registration.
  • the predefined authentication criteria includes a predefined fingerprint data of the user, a motion data associated with a predefined fingerprint signature of the user, and/or an unintentional touch data associated with the predefined fingerprint signature of the user.
  • the predefined fingerprint data of the user, the motion data associated with the predefined fingerprint signature of the user, and/or an unintentional touch data associated with the predefined fingerprint signature of the user may be registered in the electronic device 100 in advance for authenticating the user afterward.
  • the motion data is determined by at least one change of capacitance value in each portion of the display or in each cell of the fingerprint scanner when the user inputs touch gestures or the fingerprint signature on the display of the electronic device 100.
  • the unintentional touch data is determined by at least one extrapolation checkpoint corresponding to at least one cell unintentionally touched by the user, when the fingerprint signature or the touch gesture is inputted on the display of the electronic device 100.
  • the motion data may include finger pattern data and/or finger velocity data.
  • the finger velocity data may be determined by measuring a degree of changes of farad value of each portion available in the fingerprint scanner when the user provides the fingerprint signature or touch gestures on the fingerprint scanner.
  • the finger pattern data may be determined by estimating each direction and/or tracks of fingerprint based on the changes of capacitance values when the fingerprint signature is provided.
  • the finger velocity data may be determined by estimating a velocity from the starting touch 3111 to the end touch 3116 in an embodiment.
  • the finger track with the finger of the user actually made by the person other than the user may differ from a direction or velocity extractable from the registered fingerprint signature of the actual user.
  • FIG. 4 illustrates unintentional touch data obtained based on extrapolation checkpoints, according to an embodiment.
  • the unintentional touch data is obtained by at least one extrapolation checkpoint corresponding to the fingerprint signature, when the fingerprint signature is inputted on the electronic device 100.
  • the at least one extrapolation checkpoint corresponding to the fingerprint signature is encrypted.
  • FIG. 4 when user draws the fingerprint signature on the display, there can be some grid cells which can be touched unintentionally by user. It may occur due to peculiar finger movement, a change in finger portion, or other reasons.
  • the cell grids unintentionally touched by the user may also be included in the fingerprint signature pattern.
  • FIG. 4 shows five attempts of inputting a fingerprint signature and corresponding unintentional touches 4001, 4002, 4003, 4004, and 4005, respectively.
  • FIG. 5 illustrates the electronic device for authenticating the user, according to an embodiment.
  • the electronic device 100 may include a processor 110, a communicator 120, a memory 130, a display 140, an authentication engine 150, a fingerprint scanner 160, and a sensor 170.
  • the authentication engine 150 may be combined to the processor 110.
  • the display 140 may include the fingerprint scanner 160.
  • the fingerprint scanner may include the sensor 170 to sense touch inputs made by a user.
  • the fingerprint scanner 160 receives the fingerprint data and motion data of the user. Based on the received fingerprint data and the motion data, the processor 110 obtains the fingerprint data and motion data of the user via the fingerprint scanner 160. The processor 110 may obtain finger pattern data and the finger velocity data based on the obtained motion data. Then, the processor 110 may compare the obtained fingerprint data, the finger pattern data, and the finger velocity data with registered fingerprint data, registered finger pattern data, and registered finger velocity data, respectively, and may determine whether to authenticate the user based on the aforementioned comparison. The authentication engine 150 may determine whether to authenticate the user based on the matching between the fingerprint data and the motion data with the previously registered fingerprint data and the previously registered motion data.
  • the display 140 may display authentication related information (i.e., denying the authentication of the user or allowing the authentication of the user)
  • the processor 110 may calculate changes of the capacitance value in each cell sensing the fingerprint signature drawn on the fingerprint scanner 160 and mark the points of touch extrapolation (additional scanner cells touched by the finger).
  • the processor 110 may extract patterns of the user's fingerprint signature based on the changes of the capacitance value in each cell on the fingerprint scanner 160.
  • the processor 110 may execute instructions stored in the memory 130 and perform various processes caused by the instructions.
  • the communicator 120 is configured for communicating internally between internal hardware components and with external devices via one or more networks.
  • the memory 130 stores and registers the predefined fingerprint data of the user, the motion data associated with the predefined fingerprint signature of the user, and the unintentional touch data associated with the predefined fingerprint signature of the user. Further, the memory 130 stores the user fingerprint pattern related information in an optimized manner.
  • the sensor 170 can include, for example, but not limited to a capacitive fingerprint sensor, an iris sensor, a heart rate sensor, a facial recognition sensor or the like.
  • the memory 130 stores instructions to be executed by the processor 110.
  • the memory 130 may include non-volatile storage elements. Examples of such non-volatile storage elements may include magnetic hard discs, optical discs, floppy discs, flash memories, or forms of electrically programmable memories (EPROM) or electrically erasable and programmable (EEPROM) memories.
  • the memory 130 may, in some examples, be considered a non-transitory storage medium.
  • the term “non-transitory” may indicate that the storage medium is not embodied in a carrier wave or a propagated signal. However, the term “non-transitory” should not be interpreted that the memory 130 is non-movable.
  • the memory 130 can be configured to store larger amounts of information than the memory.
  • a non-transitory storage medium may store data that can, over time, change (e.g., in Random Access Memory (RAM) or cache).
  • RAM Random Access Memory
  • FIG. 5 shows various hardware components of the electronic device 100 but it is to be understood that other embodiments are not limited thereon.
  • the electronic device 100 may include less or more number of components.
  • the labels or names of the components are used only for illustrative purpose and does not limit the scope of the disclosure.
  • One or more components can be combined together to perform same or substantially similar function to authenticate the user of the electronic device 100.
  • FIG. 6 is a flow diagram for registering authentication data, , according to an embodiment.
  • the operations 601-609 may be performed by the processor 110.
  • the processor 110 may obtain, at the fingerprint scanner 160, fingerprint data and motion data of the user by at least one attempt or multiple attempts of drawing the fingerprint signature of the user.
  • the processor 110 may calculate an average of capacitance values of cells touched by the user.
  • the processor 110 may obtain finger pattern data and finger velocity data based on the motion data of the user. Likewise, the finger pattern data and the finger velocity data may be obtained as an average with multiple data obtained with several fingerprint signature or touch gesture attempts.
  • the finger pattern data and the finger velocity data may be obtained by measuring changes of capacitance values of cells in the fingerprint scanner 160.
  • the measuring of the changes of capacitance values of cells may be performed for a predetermined period of time.
  • the finger pattern data may include a set of fingerprint data with a certain direction within a predetermined range and a pressure within a predetermined range. The pressure may be extracted by measuring capacitance values of cells touched by the user.
  • the processor 110 may obtain finger acceleration data with the finger velocity data by measuring a degree(speed) of changes of capacitance values per a predetermined period of time in each cell included in the fingerprint scanner 160. In operation 609, the processor 110 may register the obtained fingerprint data, finger pattern data, and finger velocity data in the memory 130. The processor 110 may also register the obtained finger acceleration data in the memory 130. In an embodiment, the registered finger acceleration data, fingerprint data, finger pattern data, and finger velocity data may be an average value of each of the registered fingerprint data, finger pattern data, and finger velocity data obtained by several attempts. The registered data may be stored in the memory 130. Further, the processor 110 may obtain and register at least one unintentional touch data.
  • the at least one unintentional data may be determined when cells are touched in drawing the fingerprint signature at one attempt and not touched in another attempt.
  • the capacitance values corresponding to the unintentional data may be obtained by extrapolation with neighboring points (cells) to the extrapolated cells where the neighboring cells are touched and thus have the capacitance values at every attempt for registration.
  • the processor 110 may obtain data such as a heart rate of the user, face pattern of the user, iris data of the user, a grip pressure of the user, and body temperature of the user as supplementary data or secondary data for achieving the delicate authentication of the user.
  • the obtained supplementary data may be registered as a threshold value for a future comparison with the inputted same type of supplementary data.
  • FIG. 7 illustrates a flow diagram for authenticating the user of the electronic device, according to an embodiment.
  • the processor 110 may obtain, via the fingerprint scanner 160, fingerprint data and motion data of the user while the user draws his/her fingerprint signature or touch gestures.
  • the track of touch gestures may be predetermined by registering it in advance.
  • the processor 110 may obtain finger pattern data and finger velocity data based on the motion data of the user.
  • the finger pattern data and the finger velocity data may be obtained by measuring changes of capacitance values in each cell of the fingerprint scanner 160. The measuring of the changes of capacitance values in each cell may be performed over a predetermined period of time.
  • the finger pattern data may include a set of fingerprint data with a certain direction within a predetermined range and a pressure within a predetermined range. The pressure may be extracted by measuring capacitance values of cells touched by the user.
  • the processor 110 may obtain a capacitance value of extrapolation cells among cells of the fingerprint scanner based on measuring changes of capacitance values in cells which are unintentionally touched by the user.
  • the extrapolation cells may be the cells which are unintentionally touched by the user.
  • the processor 110 may obtain finger acceleration data with the finger velocity data by measuring a degree(speed) of changes of capacitance values per a predetermined period of time in each cell of the fingerprint scanner 160 where the finger acceleration data is compared with registered finger acceleration data.
  • the processor 110 may obtain secondary data such as a heart rate of the user, face pattern of the user, iris data of the user, a grip pressure of the user, and body temperature of the user as supplementary information for achieving the delicate authentication of the user.
  • the grip pressure of the user may indicate pressure data when the user grips edges of the electronic device 100 while inputting user's fingerprint on the fingerprint scanner.
  • a pressure sensor may be implemented on the edge of the electronic device 100.
  • the processor 110 may determine that the user is in the unawareness state such as sleeping. Likewise the body temperature may relative fall down during a sleep state.
  • the processor 110 may compare the fingerprint data, the finger pattern data, and the finger velocity data with registered fingerprint data, registered finger pattern data, and registered finger velocity data, respectively. Additionally, the finger acceleration data can be compared with the registered finger acceleration data. In operation 709, the processor 110 may determine whether to authenticate the user based on the comparison result.
  • FIGS. 8A, 8B, 8C, and 8D are example illustrations of an encryption process, according to an embodiment.
  • the electronic device 100 captures an event (e.g., touch event) of the user moving his/her finger from 8001 to 8002 during ⁇ t1.
  • the processor 110 of the electronic device 100 calculates the change of capacitance values across the fingerprint sensor grid for ⁇ t1 and can save the movement from 8001 to 8002 as function of a change of capacitance value/ ⁇ t.
  • first touch grid 8100 corresponds to 4x4 cells covered by the starting touch 8001
  • second touch grid 8200 corresponds to 4x4 cells covered by the next touch 8002.
  • the grid 8700 illustrates a capacitance value difference at a cell touched by the user for ⁇ t1.
  • the value of the grid(cell) 871 represents the capacitance value(farad value) difference between 811 and 821 caused by a finger movement from 8001 to 8002.
  • a finger movement from 8002 to 8003 is captured and the change in farad values(capacitance values)/ ⁇ t2 is obtained by the processor 110 and saved in the memory 130.
  • the value of the block 881 represents the capacitance value(farad value) difference between 822 and 832 caused by a finger movement from 8002 to 8003.
  • a finger movement from 8003 to 8004 is captured and the change in farad values/ ⁇ t3 is obtained by the processor 110 and saved in the memory 130.
  • the value of the block 882 represents the capacitance value(farad value) difference between 832 and 842 caused by a finger movement from 8003 to 8004.
  • a finger movement from 8004 to 8005 is captured and the change of farad value(capacitance value)/ ⁇ t4) is obtained and saved in the memory 130.
  • the value of the block 883 represents the capacitance value(farad value) difference between 842 and 852 caused by a finger movement from 8004 to 8005.
  • the finger movement from 8001 to 8002, 8002 to 8003, 8004 to 8004 is illustrated to move 1 grid distance during ⁇ t1, ⁇ t2, ⁇ t3, and ⁇ t14, the movement distance is not limited thereto.
  • the finger movement from 8001 to 8002 may be 2-grid distance
  • the finger movement from 8002 to 8003 may be 3-grid distance
  • the finger movement from 8003 to 8004 may be 3 grid distance in a diagonal direction.
  • the processor 110 may detect the finger movement distance based on the capacitance value difference per a predetermined period of time or a degree of the capacitance value changes per a predetermined period of time. For example, referring back to FIG. 8A and 8B, if the finger moves from 8002 to 8004 for ⁇ t1, the processor 110 may analyze that the movement is two times faster than that of FIG. 8A. Based on the movement analysis, the processor 110 may determine to whether authenticate the user.
  • FIG. 9 is an example illustration of an acceleration pattern during the registration of sensed data, according to an embodiment.
  • the sensor inputs are used as data to learn the user's fingerprint signature acceleration pattern.
  • the five blocks 9010 represents five attempts of an acceleration data input.
  • the block 9020 represents the learned acceleration encryption data of each cell created by the fingerprint signature.
  • FIG. 10, FIG. 11 and FIG. 12 illustrate example scenarios in which the electronic device authenticates the user using various supplementary information, according to an embodiment.
  • a wearable device 10010 may be used to obtain supplementary information such as a heart rate of the user of the electronic device 1000 or a body temperature of the user of the electronic device 100.
  • the obtained heart rate or the body temperature may be used in combination with the sensed data - fingerprint data for authenticating the user.
  • the supplementary information may assist in determining whether the user is in an aware state or not.
  • the electronic device 100 uses a grip/pressure sensor 11010 with the fingerprint sensor.
  • the grip/pressure sensor 11010 detects user's grip pressure pattern over the period of time, especially when the user is in an aware state. Thus, in the grip/pressure data may enhance the level of security in authenticating the user.
  • the electronic device 100 uses the facial recognition sensor 12010 with the fingerprint sensor.
  • the facial recognition sensor 12010 performs facial recognition along with the fingerprint data of the user, the motion data associated with the fingerprint signature of the user, and the unintentional touch data associated with the fingerprint signature of the user. This results in enhancing the level of security of the electronic device 100 by checking the face of the user as supplementary information in authenticating the user
  • the electronic device 100 may use an iris scan sensor with the fingerprint sensor.
  • the iris scan sensor detects user's iris pattern as supplementary information.
  • the embodiments disclosed herein can be implemented using at least one software program running on at least one hardware device and performing network management functions to control the elements.

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Abstract

La présente invention concerne un procédé et un appareil d'authentification d'un utilisateur. Le procédé comprend : l'obtention, au niveau d'un lecteur d'empreintes digitales, de données d'empreintes digitales et de données de mouvement de l'utilisateur ; l'obtention de données de modèle de doigt et de données de vitesse de doigt sur la base des données de mouvement de l'utilisateur ; la comparaison des données d'empreintes digitales, des données de modèle de doigt et des données de vitesse de doigt respectivement à des données d'empreintes digitales enregistrées, à des données de modèle de doigt enregistrées et à des données de vitesse de doigt enregistrées ; et la détermination de l'authentification ou non de l'utilisateur sur la base de la comparaison.
PCT/KR2020/016098 2019-11-22 2020-11-16 Procédé et appareil d'authentification d'un utilisateur WO2021101192A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100119124A1 (en) * 2008-11-10 2010-05-13 Validity Sensors, Inc. System and Method for Improved Scanning of Fingerprint Edges
KR20150061449A (ko) * 2013-11-27 2015-06-04 엘지전자 주식회사 전자 기기 및 전자 기기의 제어 방법
WO2017063763A1 (fr) * 2015-10-14 2017-04-20 Secure Fingerprints As Authentification biométrique sécurisée
US20170180988A1 (en) * 2015-12-21 2017-06-22 Samsung Electronics Co., Ltd. User authentication method and apparatus
KR20180078711A (ko) * 2016-12-30 2018-07-10 주식회사 베프스 손가락 움직임과 지문 동시 인식에 기반한 사용자 인증 보안 장치 및 이를 이용한 거래 방법

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100119124A1 (en) * 2008-11-10 2010-05-13 Validity Sensors, Inc. System and Method for Improved Scanning of Fingerprint Edges
KR20150061449A (ko) * 2013-11-27 2015-06-04 엘지전자 주식회사 전자 기기 및 전자 기기의 제어 방법
WO2017063763A1 (fr) * 2015-10-14 2017-04-20 Secure Fingerprints As Authentification biométrique sécurisée
US20170180988A1 (en) * 2015-12-21 2017-06-22 Samsung Electronics Co., Ltd. User authentication method and apparatus
KR20180078711A (ko) * 2016-12-30 2018-07-10 주식회사 베프스 손가락 움직임과 지문 동시 인식에 기반한 사용자 인증 보안 장치 및 이를 이용한 거래 방법

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