WO2011072284A1 - Détection de la fréquence du pouls au moyen d'un détecteur d'empreintes digitales - Google Patents

Détection de la fréquence du pouls au moyen d'un détecteur d'empreintes digitales Download PDF

Info

Publication number
WO2011072284A1
WO2011072284A1 PCT/US2010/060073 US2010060073W WO2011072284A1 WO 2011072284 A1 WO2011072284 A1 WO 2011072284A1 US 2010060073 W US2010060073 W US 2010060073W WO 2011072284 A1 WO2011072284 A1 WO 2011072284A1
Authority
WO
WIPO (PCT)
Prior art keywords
image
fingerprint
time series
pulse
processor
Prior art date
Application number
PCT/US2010/060073
Other languages
English (en)
Inventor
Ronald Kropp
Richard Irving
Rainer M. Schmitt
Original Assignee
Sonavation, Inc.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Sonavation, Inc. filed Critical Sonavation, Inc.
Publication of WO2011072284A1 publication Critical patent/WO2011072284A1/fr

Links

Classifications

    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/024—Detecting, measuring or recording pulse rate or heart rate
    • A61B5/02444—Details of sensor
    • 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
    • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/117—Identification of persons
    • A61B5/1171—Identification of persons based on the shapes or appearances of their bodies or parts thereof
    • A61B5/1172—Identification of persons based on the shapes or appearances of their bodies or parts thereof using fingerprinting

Definitions

  • the present invention generally relates to biometrics.
  • biometric characteristics A variety of well known techniques exist for sensing, measuring, and identifying biometric characteristics. These techniques focus on unique characteristics associated with structures that form the biometrics, and which can therefore uniquely identify an individual. By way of one example, fingerprints, defined by ridges and valleys in a finger, are one such biometric.
  • a pulse rate defined as the number of beats-per-minute of the heart, is a measurable physiological function.
  • the sensors used to measure biometric information are different in structure and function from those used to measure physiological activities.
  • the optical imaging, capacitative, and piezoelectric devices typically used to detect fingerprints are quite different in structure, design and operation from the plethysmographic or infrared devices used to measure a pulse.
  • a person carrying a personal digital support device, a medical device, or a communication device may wish to identify themselves to the device via biometric identification. At other times, the same person may also wish to use the device to monitor a physiological parameter such as pulse, for example for medical monitoring purposes. 100071 What is needed, therefore, are systems and methods that enable pulse detection in devices conventionally configured for fingerprint detection.
  • the present invention includes a fingerprint sensor, which may be a standalone fingerprint sensor or a fingerprint sensor coupled with or integrated into another technology (for example, a cell phone or a computer).
  • the fingerprint sensor includes a biometric sensing element for the fingerprint detection, and can include, or be coupled to, a processor.
  • the system of fingerprint sensor and processor may also contain, or be coupled to, a memory configured to store image data and/or to store instructions for processing image data.
  • the biometric sensing element is configured to obtain, and to send to the processor and/or memory, a time series of images of at least a portion of a fingerprint. Using the processor, earlier and later images from the time series of images of the fingerprint are compared. Differences in the time series of images are identified.
  • the present invention determines whether the differences in images are so large as to be the result of bulk motion of a finger or other digit in relation to the biometric sensor, in which case the images are not used to obtain heart rate data. If, in bulk aspect, the digit is substantially stationary relative to the biometric sensor, then smaller, more subtle changes in the fingerprint over time are detected. These changes in the fingerprint are induced by the pulse of blood through the digit, and may be either small changes in the pressure of ridges from the digit, or small changes in the spatial location of the ridges relative to pixels or coordinate locations on the biometric sensor, or both.
  • the changes in the fingerprint are analyzed to determine a substantially cyclical pattern which is substantially stable over a short period of time (for example, over several seconds), and which falls within a typical range for a pulse.
  • a substantially cyclical pattern of the change in the fingerprint within an acceptable range and of acceptable stability, is identified as being a result of the pulse.
  • the identified pulse may then be displayed to a user, stored in data storage, transmitted, etc.
  • FIG. 1 A is an illustration of an exemplary fingerprint during a period of diastolic pressure (low pressure).
  • FIG. IB is an illustration of the exemplary fingerprint of FIG. 1A during a period of systolic pressure (high pressure).
  • FIG. 1 C is an illustration of another exemplary fingerprint during a period of diastolic pressure (low pressure).
  • FIG. ID is an illustration of the exemplary fingerprint of FIG. 1 C during a period of systolic pressure (high pressure).
  • FIG. 2A is an illustration of an exemplary fingerprint sensor.
  • FIG. 2B is an illustration of another exemplary fingerprint sensor.
  • FIG. 3 is an illustration of an exemplary platen of an exemplary fingerprint sensor.
  • FIG. 4 is a system diagram of an exemplary processing system which is part of an exemplary fingerprint sensor.
  • FIG. 5A illustrates an exemplary time series of images of a fingerprint or a portion of a fingerprint as captured by an exemplary platen of an exemplary fingerprint sensor.
  • FIG. 5B illustrates subtracting a first, earlier fingerprint image frame from a second, later fingerprint image frame to create a difference frame.
  • FIG. 6 is a flowchart of an exemplary method of detecting a pulse with a fingerprint sensor.
  • FIG. 7 is a detailed flowchart of one of the steps from the exemplary method of the flowchart of FIG. 6, specifically, a flowchart of an exemplary method to process fluctuations in a fingerprint to determine an instantaneous fluctuation intensity.
  • FIG. 8 is a detailed flowchart of one of the steps from the exemplary method of the flowchart of FIG. 6, specifically a flowchart of an exemplary method to calculate a pulse rate based on a time series of instantaneous fluctuation intensities.
  • fingerprints are defined by unique structures on the surface of the finger called ridges and valleys.
  • FIGs. 1A and IB are illustrations of the basic structure 100 of a fingerprint
  • the basic fingerprint structure 100 includes ridges 102 and valleys 104 (illustrated as ridges 102a, 102b and valleys 104a, 104b, respectively), which combine to form an entire fingerprint (elements 106a, 106b of FIGs. 1 C and ID, discussed below).
  • Ridges 102 are fingerprint tissue
  • valleys 104 are regions or empty space (or, typically air) between the ridges.
  • the invention is directed towards a system and method for detecting the pulse of a mammal, such as a person, using a sensor which is conventionally configured as a fingerprint sensor.
  • FIGs. 1A and IB fingerprint structure 100 is shown pressed against a platen
  • FIG. 1 10 i.e. a sensing surface of a fingerprint sensor.
  • Exemplary fingerprint sensors, including platen 1 10, are described in further detail below in conjunctions with FIGs. 2A, 2B, 3, and 4.
  • FIGs. 1 C and ID illustrate a complete fingerprint 106 of a digit of a mammal
  • FIG. 1A is distinguished from FIG. I B in that FIG. 1A represents the fingerprint tissue during an interval of diastolic pressure (low blood pressure) when the heart is at rest during a cardiac cycle.
  • FIG. IB represents the fingerprint tissue during an interval of systolic pressure (high blood pressure) when the heart is pumping blood at maximum pressure during a cardiac cycle.
  • FIGs. 1 C and ID are distinguished by representing the fingerprint during diastolic pressure and systolic pressure, respectively.
  • the present invention is based on variations in the fingerprint between the diastolic and systolic points of the cardiac cycle.
  • the ridges 102 of fingerprint 106 will be closer together, and fingerprint 106 will exert less pressure on platen 1 10.
  • the ridges 102 of fingerprint 106 will expand further from each other and ridges 102 will place higher pressure and be broader in their signature against platen 1 10.
  • FIG. 1 A compared to FIG. IB illustrates the difference in the fingerprint structure
  • FIGs. l C and I D illustrates the change in the fingerprint 106 between the diastolic and systolic intervals of the cardiac cycle.
  • the area of contact between the ridges 102 and platen 1 10 may be increased during the systole as compared to the diastole, and the fingerprint structure 100 or fingerprint as a whole 106 may be distended during systole as compared with diastole.
  • the ridges 102 and valleys 104 of fingerprint 106 can be sensed, measured, and identified based upon a number of different modalities. These modalities sense differences between the fingerprint ridges and valleys with respect to dielectric permittivity, thermal conductivity, acoustic impedance, optical index, or other sensing modalities.
  • some fingerprint measurement modalities rely on density values associated with the ridges and valleys.
  • Acoustic impediography employing for example piezoelectric sensing elements, is one means to distinguish the density variations between ridges and valleys.
  • dielectric permittivity As measured when an electric current is passed through the ridges and valleys.
  • dielectric permittivity for example, the permittivity of a ridge (i.e., tissue), is different from the permittivity of a valley (i.e., air between the ridges).
  • Capacitive sensing is one technique that can be used to detect changes in pemiittivity. With capacitive sensing, capacitance values generated when a sensor plate (electrode) touches a ridge are different than those generated when the sensor is exposed to a valley.
  • thermal conductance which is a measure of the temperature differences between the ridges and valleys.
  • Optics are yet another modality.
  • Optical techniques rely on an optical index of refractive and reflective changes between the ridges and the valleys.
  • impressions of a fingerprint is any data which may be captured by platen 1 10, and which is descriptive of or otherwise captures or is indicative of the structure of the fingerprint, which may include the shape of the fingerprint (as defined by the ridges and valleys), the pressure exerted by the fingerprint, and/or other structural aspects of the fingerprint.
  • data may include, for example and without limitation, fingerprint image data, fingerprint pressure data, dielectric permittivity data associated with the fingerprint, capacitive data associated with the fingerprint, thermal conductance data associated with the fingerprint, and/or optical index data associated with the fingerprint.
  • FIG. 2A illustrates an exemplary fingerprint sensor 200a.
  • exemplary fingerprint sensor 200a may be a standalone fingerprint sensor, or can be combined with or integrated into another electronic device.
  • fingerprint sensor 200a can be part of a portable communications device such as a cell phone or personal digital assistant.
  • Fingerprint sensor 200a can also be part of a personal computer or laptop computer.
  • elements of fingerprint sensor 200a can serve additional purposes other than those specifically indicated herein.
  • a display element 215, discussed further below, can provide display functions not only for fingerprint sensor 200a, but for the cell phone, personal digital assistant, laptop computer, or other similar device.
  • Fingerprint sensor 200a can be integrated into these other devices, in part or in whole because fingerprint sensor 200a can serve to provide biometric identification and security for the device.
  • Fingerprint sensor 200a includes platen or sensor pad 1 10a (discussed above in conjunction with FIG. 1), a display 215, a control panel 220, and a processing system 225.
  • Platen 1 10a can also be referred to as a sensor pad, a sensor plate, or as a biometric sensing element.
  • Platen 1 10a is configured to receive a digit of a person or other mammal, such as a finger of a person.
  • Platen 1 10a is configured with sensing elements, discussed further below, which are capable of detecting distinguishing elements of fingerprint 106.
  • platen 1 10a is configured to detect ridges 102 and valleys 104 of fingerprint 106.
  • the platen 1 10a is sufficiently wide to receive a full width of fingerprint 106, but can be narrow enough in height to only receive a portion of a vertical section of fingerprint 106. Therefore, fingerprint sensor 1 10a can require that to detect fingerprint 106, a user must swipe or scroll their finger across platen 110a.
  • platen 1 10a is configured to return an image of fingerprint 106 consisting of pixel elements as discussed further below in conjunction with FIG. 3. Platen 1 10a is also configured to return a time series of images of fingerprint 106. In alternative embodiments, platen 1 10a can be configured to return data other than image data which is descriptive of the structure of fingerprint 106. Such data may be in alternative to, or in addition to, image data of fingerprint 106. As noted above, the data captured by platen 110a and which is descriptive of the structure 100 of fingerprint 106 is generically referred to as an impression of fingerprint 106.
  • Sensor 200a includes display element 215.
  • Display element 215 can be used to display a variety of information pertinent to the use of fingerprint sensor 200a.
  • display 215 can display instructions for the use of fingerprint sensor 200a.
  • Display 215 can also display a result of a fingerprint detection, for example, a biometric determination of a user.
  • Control panel 220 can include a variety of controls used by fingerprint sensor 200a.
  • control panel 220 can include controls to initiate a scan of a fingerprint, and can include controls to initiate a pulse detection using fingerprint scanner 200a.
  • Fingerprint sensor 200a also includes a processing system 225, shown in FIG. 2A with a dotted outline.
  • the dotted line indicates that processing system 225 is typically internal to fingerprint sensor 200a.
  • Processing system 225 includes a variety of elements necessary to process fingerprint scanning information, and also to detect and process a cardiac pulse. The details of processing system 225 are discussed below in greater detail in conjunction with FIG. 4. As will be discussed in detail below, processing system 225 is configured to detect a variation over time of fingerprint 106, or in a portion of a fingerprint 106, returned by platen 1 10.
  • processing system 225 is configured to detect the variation over time in fingerprint 106 by comparing images or other fingerprint impressions from a time series of images, or time series of impressions, returned by platen 1 10.
  • FIG. 2B is a view of another exemplary fingerprint sensor 200b. Most of the elements of exemplary fingerprint sensor 200b are the same as elements of exemplary fingerprint sensor 200a discussed above, and the details of these elements will not be repeated here.
  • One distinguishing feature between fingerprint sensor 200b and 200a is a sensor platen 1 10b. Sensor platen 110b is configured to be both tall enough and wide enough to receive an entire fingerprint. Therefore, in conjunction with sensor 200b and platen 110b, a user would not be required to move or swipe their finger across platen 1 10b.
  • exemplary fingerprint sensors 200a and 200b may include other elements not specifically illustrated or discussed, such as for example power elements, various ports for communications with other technologies (and which may serve as additional control elements), and other elements as well.
  • FIG. 3 illustrates a detailed view of exemplary platen 110, which may correspond for example to platen 1 10 discussed above in conjunction with FIGs. 1A and IB, platens 110a or 1 10b discussed above in conjunction with FIGs. 2A and 2B, or to similar platens configured for fingerprint sensing.
  • platen 1 10 is the fingerprint sensing element of exemplary fingerprint sensors 200a, 200b.
  • Platen 1 10 is configured to receive a digit of a mammal, such as a person, in order to detect the fingerprint of the person. Additionally, platen 1 10 is configured to detect a pulse associated with the received digit based upon variations in the fingerprint.
  • platen 1 10 may employ a variety of technologies in order to return data descriptive of fingerprint 106.
  • Platen 1 10 returns data descriptive of fingerprint 106 by detecting the ridges 102 and valleys 104 of fingerprint 106.
  • These detection technologies can, for example, be capacitive, dielectric, optical, thermal, or acoustic. Other technologies can be employed as well. No matter which sensing technology is employed, platen 1 10 will typically be divided into discrete pixels 305.
  • Pixels 305 can constitute either discrete physical sensing elements or logical image elements extracted from a fingerprint image or other fingerprint impression.
  • each pixel is represented by a small square, while circular black dots provide a representative illustration of a large plurality of pixels (larger than the number of black dots shown) which would too numerous to fit in the space of the illustration.
  • platen 1 10 can be divided into eight rows 315 of pixels, and
  • Each pixel 305 is configured to return an image density which is indicative of a presence of a ridge 102 or valley 104 of a fingerprint 106.
  • a pixel 305 fully in contact with a ridge 102 will return a fully darkened color or least magnitude signal, while a pixel 305 in contact with a valley 104 may return fully light, fully white, maximum intensity signal or image intensity.
  • a pixel 305 in contact with a ridge 102 will return a maximum image intensity while a pixel 305 in contact with a valley 104 may return a minimum image intensity or signal intensity.
  • a pixel 305 is typically configured to return a signal or image intensity in a predetermined range.
  • a pixel 305 can be configured to return an 8-bit value resulting in image intensities ranging from zero to 255 or one to 256.
  • the pattern of image intensities or signal intensities on pixels 305 is used by processing system 225 to identify a fingerprint of a mammal.
  • the pattern of pixels 305 on platen 1 10 can also be used by processing system 225 to detect the pulse of the mammal.
  • FIG. 4 illustrates an exemplary processing system 225 which can be part of a fingerprint sensor 200.
  • Processing system 225 can be configured, through suitable instructions stored in memory or hard coded into firmware, to detect a fingerprint of a mammal.
  • Processing system 225 may also be configured, through suitable instructions stored in memory or hard coded into firmware, and as described in further detail below, to detect a pulse of a mammal and determine the pulse rate of the mammal. If fingerprint sensor 200 is combined with or integrated into another technology, for example a cell phone, a personal digital assistant, or a computer, processing system 225 may be configured to perform additional processing tasks as well.
  • Processing system 225 includes one or more processors, which may be a microprocessor, such as processor 404.
  • processor 404 is configured to detect a variation over time of fingerprint 106, or in a portion of a fingerprint 106, returned by platen 1 10.
  • processor 404 is configured to detect the variation over time in fingerprint 106 by comparing images or other fingerprint-descriptive data from a time series of images, or time series of descriptive data, returned by platen 1 10.
  • Processor 404 is connected to a communication infrastructure 406 (e.g., a communications bus, cross over bar, or network).
  • a communication infrastructure 406 e.g., a communications bus, cross over bar, or network.
  • the algorithms described herein for detecting a pulse are typically implemented in various software embodiments, which may be executed by exemplary processing system 225 using processor 404. After reading this description, it will become apparent to a person skilled in the relevant art(s) how to implement the invention using other processing systems and/or architectures.
  • Processing system 225 can include a display interface 402 that forwards graphics, text, and other data from the communication infrastructure 406 (or from a frame buffer not shown) for display on the display unit 215.
  • Processing system 225 can include a platen interface 432 that receives biometric data and possibly other data from fingerprint platen 1 10, and that may also be configured to send configuration or control data to fingerprint platen 1 10.
  • Processing system 225 can include a control interface 434 that receives control data and control instructions from fingerprint sensor controls 220, and which may also send data to controls 220 (for example, state controls, status display signals, etc.).
  • controls 220 for example, state controls, status display signals, etc.
  • Processing system 225 can include timer/clock element 436.
  • Timer/clock element 436 Timer/clock element
  • Timer/clock 436 may be configured to maintain a current system date and time, and may also be configured to determine or report the time interval between two or more designated events. Timer/clock element 436 may also be configured for related timing determinations, such as determination of frequencies of designated events. In an embodiment, timer/clock 436 may be a separate element from processor 404. In an alternative embodiment, timer/clock may be integrated into processor 404. In an alternative embodiment, timer/clock 436 may be an external element which delivers a clock signal and timing signals or timing data to processing system 225, for example via communications interface 424 or another interface. In an alternative embodiment, timer/clock 436 may be part of processing system 225, but may be supplemented by external synchronization or timing data.
  • Processing system 225 also includes a main memory 408, preferably random access memory (RAM), and may also include a secondary memory 410.
  • the secondary memory 410 may include, for example, a hard disk drive 412 and/or a removable storage drive 414, representing a floppy disk drive, a magnetic tape drive, an optical disk drive (CD/DVD/BlueRay drive), USB slot, etc.
  • the removable storage drive 414 reads from and/or writes to a removable storage unit 418 in a well known manner.
  • Removable storage unit 418 represents a floppy disk, magnetic tape, optical disk (CD/DVD/BlueRay, etc.), flash drive, etc. which is read by and written to by removable storage drive 414.
  • the removable storage unit 418 includes a computer usable storage medium having stored therein computer software and/or data.
  • secondary memory 410 may include other similar devices for allowing computer programs or other instructions to be loaded into processing system 225.
  • Such devices may include, for example, a removable storage unit 422 and an interface 420. Examples of such may include a program cartridge and cartridge interface (such as that found in video game devices), a removable memory chip (such as an erasable programmable read only memory (EPROM), or programmable read only memory (PROM)) and associated socket, and other removable storage units 422 and interfaces 420, which allow software and data to be transferred from the removable storage unit 422 to processing system 225.
  • a program cartridge and cartridge interface such as that found in video game devices
  • EPROM erasable programmable read only memory
  • PROM programmable read only memory
  • Processing system 225 may also include a communications interface 424.
  • Communications interface 424 allows software and data to be transferred between processing system 225 and external devices.
  • Examples of communications interface 424 may include a modem, a network interface (such as an Ethernet card), a communications port, a Personal Computer Memory Card International Association (PCMCIA) slot and card, etc.
  • Software and data transferred via communications interface 424 are in the form of signals 428 which may be electronic, electromagnetic, optical or other signals capable of being received by communications interface 424. These signals 428 are provided to communications interface 424 via a communications path (e.g., channel) not shown. This channel carries signals 428 and may be implemented using wire or cable, fiber optics, a telephone line, a cellular link, an radio frequency (RF) link and other communications channels.
  • RF radio frequency
  • “computer usable physical storage medium,” “computer readable physical storage medium,” and similar terms are used to generally refer to media such as a hard disk installed in hard disk drive 412, removable storage drive 414, interface 420, and the physical removable storage media 418, 422 such as CDs, DVDs, tape drives, flash memory, program cartridge, PCMCIA card, and similar physical media which may be inserted or installed into storage drive 414 or connected or coupled to interface 420.
  • These physical computer program products provide software to processing system 225. The invention is directed in part to such computer program products.
  • Computer programs are stored in main memory 408 and/or secondary memory 410. Computer programs may also be received via communications interface 424. Such computer programs, when executed, enable the processing system 225 to perform the features of the present invention, as discussed herein. In particular, the computer programs, when executed, enable the processor 404 to perform the features of the present invention directed towards detecting a pulse of a mammal and related functions. Accordingly, such computer programs represent controllers of the processing system 225.
  • the software may be stored in a computer program product and loaded into processing system 225 using removable storage drive 414, hard drive 412, interface 420, removable storage units 418, 422, or communications interface 424.
  • the control logic when executed by the processor 404, causes the processor 404 to perform the functions of the invention to detect a pulse of a mammal and related functions as described herein.
  • the invention is implemented primarily in hardware using, for example, hardware components such as application specific integrated circuits (ASICs). Implementation of the hardware state machine so as to perform the functions described herein will be apparent to persons skilled in the relevant art(s). [0073] In yet another embodiment, the invention is implemented using a combination of both hardware and software.
  • ASICs application specific integrated circuits
  • FIG. 5 A is an exemplary series of images 500 from a sensor platen 1 10 of fingerprint sensor 200.
  • the series of images 500 is a time series of images of a fingerprint 106.
  • the images are taken at a frame rate of 20 slices per second or 20 frames per second, that is, once every 50 milliseconds.
  • 20 frames per second has been found to be an effective rate for detecting pulse-induced variations in the fingerprint image.
  • a frame rate of 20 frames per second is sufficient to enable detection of gross movement or bulk movement of a person's finger, which interferes with the pulse detection process, as discussed further below.
  • a frame rate of 20 slices per second is, approximately, the lowest or slowest frame acquisition rate that will produce reliable results for detecting a pulse.
  • An advantage of embodiments employing this lowest frame rate of approximately 20 slices per second (or equivalently, twenty frames per second) is that it requires less processing and few frame buffers than higher frame rates. Especially for portable devices, where power and memory may be limited, the conservation of power and memory may be advantageous.
  • Embodiments described immediately below are based on an exemplary frame rate of 20 slices per second, it being understood that the system and method may be suitably adapted for higher frame rates.
  • Series of images 500 is a series over eleven consecutive time slices of a fingerprint, numbered 501 through 51 1 , with some time slices (506 through 510) deliberately omitted. It can be seen in successive images, starting from 501 to 502, 502 to 503, and so forth, that as a result of variations in the blood pressure in a person's finger, the image of the fingerprint varies from frame to frame. Image areas corresponding to ridges and valleys have been labeled as 592 (ridges) and 594 (valleys).
  • slices 501 is representative of a fingerprint image at or near diastole
  • slice 505 is representative of a fingerprint image at or near systole
  • Slice 51 1 is representative of a fingerprint image during a period of pressure transition marking a return from systole to diastole.
  • time series 500 is representative only, and may not conform in all respects to an actual image of a fingerprint or section of a fingerprint.
  • the number of ridges shown and the spacing between ridges is intended for purposes of illustration only, and may vary from that of a real fingerprint.
  • the variation in ridges 102/592 and valleys 104/594 over time, due to blood pressure changes, has been exaggerated for purposes of illustration.
  • the fingerprint image can vary both in the location of a ridge 102 (as shown by the location of ridge images 512), or valley 104 (as shown by the location of valley images 514).
  • the image can also vary in the intensity of a pixel 305 under a ridge 102. Both location variations and intensity variations are induced by the changes in the blood pressure as a result of the pulse.
  • the first four images are received into a frame buffer which may for example be stored in memory 408.
  • the third preceding image in the buffer i.e., three frames back
  • the third preceding image in the buffer i.e., three frames back
  • variations in the fingerprint image over time are detected.
  • frame 501 would be subtracted.
  • frame 505 would be subtracted.
  • frame 502 would be subtracted.
  • frame 503 would be subtracted.
  • the number of images received into the frame buffer, and the interval between the first frame and second frame can vary, for example, five frames, six frames, etc.
  • the interval between the first frame and the second frame may be greater still, for example, 10 frames or 20 frames, or some other number as may be determined through testing.
  • subtraction of a first frame from a second frame entails subtracting, on a basis of corresponding pixels, the pixel value of a first frame from the pixel value of a second frame.
  • Frame 5XX refers to an image from a generic time slice (at some frame count XX)
  • frame 5XX-N refers to an image from a time slice N frames earlier.
  • 5XX-3 refers to an image slice or image frame 3 frames earlier than frame 5XX.
  • embodiments of the present invention entail determining a time series of such difference frames 551 , and then comparing the difference frames to determine a pulse rate.
  • Each difference frame in the time series is determined based on a difference between a current frame, represented symbolically as 5XX, and a frame several frames earlier, for example frame 5XX - 3.
  • processing system 225 and processor 404 in particular, are configured to detect a variation over time of fingerprint 106, or in a portion of a fingerprint 106, returned by platen 1 10.
  • processing system 225 is configured to detect the variation over time in fingerprint 106 by comparing fingerprint-descriptive data from a time series of fingerprint descriptive data returned by platen 1 10. By comparing descriptive data from discrete time slices of the time series, processing system 225 generates a series of data values which are descriptive of fluctuations in fingerprint 106 over time. This series of data values, which represent fluctuations in fingerprint 106 over time, are further analyzed by processing system 225 to determine the pulse of a person or other mammal associated with fingerprint 106.
  • the fingerprint descriptive data obtained from platen 1 10 is image data 5XX of fingerprint 106.
  • image data 5XX of fingerprint 106 By comparing successive images 5XX, a series of values are generated which are descriptive of differences between the images 5XX. The values are referred to as "instantaneous fluctuation intensities.” Further analysis of the series of instantaneous fluctuation intensities yields the pulse.
  • FIG. 6 is a flowchart of an exemplary method 600 for detecting a pulse with a fingerprint sensor. The method 600 begins at step 605.
  • the sensor is adjusted for image sensitivity and contrast, which is optimized for pulse detection.
  • the sensor circuitry is adjusted so that the background image corresponding to regions of air (valleys 104) is represented as white (valley images 594).
  • the background image is adjusted to have a grayscale value of 255.
  • the circuitry is also adjusted for high sensitivity and high contrast so that the image is approximately 20% binarized, meaning that approximately 20% of the fingerprint image is either completely white or completely black. This maximizes the sensitivity, but leaves enough grayscale content so that pressure fluctuations can be measured.
  • the sensor is adjusted to have a partially saturated image so that valleys 104 appear white and ridges 102 appear black.
  • step 615 the frame rate is adjusted for pulse detection.
  • the sensor typically, the senor
  • a frame rate of 20 slices per second is sufficient to detect a pulse.
  • a frame rate greater than 20 slices per second may be employed. Theoretically, the only upper limit to the sensor frame rate is imposed by
  • Step 620 entails obtaining a time series of images of a digit, that is, obtaining a time series of slices of a fingerprint.
  • the process either begins at step 20 or, if the process is continuing, it continues at step 620.
  • An exemplary time series of fingerprint images 500 is discussed above in conjunction with FIGs. 5A and 5B.
  • time delayed background image subtraction is done on the time series of images. As indicated above (see discussion of FIGs. 5A and 5B), this time delayed image subtraction is typically performed by subtracting from each frame the frame which preceded it by three frames earlier. Thus, the fourth frame would have subtracted from it the first frame, the fifth frame would have subtracted the second frame, the sixth frame would have subtracted from it the third frame.
  • the resulting series of images which are a series of difference frames 551 , are stored in a difference frame buffer, for example in memory 408, for further processing.
  • An optional step entails a normalization of the difference. For example, if a pixel has a same value in a later time slice than an earlier time slice, rather than the difference pixel value being zero, the difference value for that pixel may be normalized to an intermediate value (for example, 140 on a scale from 0 to 255). With a 0-normalization to 140, a positive difference (that is, a positive result of subtracting the earlier pixel from the later pixel) will result in a difference pixel value in a range from 141 to 255.
  • a negative difference that is, a negative result of subtracting the earlier pixel from the later pixel
  • a difference pixel value in a range from 0 to 139.
  • 0-normalization values e.g., other than 140
  • step 630 a determination is made based on the series of difference frames 551 as to the coupling stability between the digit of the mammal, or the finger of the mammal, and the platen 1 10. The details of step 630 are discussed in further detail below.
  • the method requires that the mammalian digit in bulk, that is, the digit taken as a whole, be substantially stable and not in motion, that is, that the digit be substantially static in relation to platen 1 10. While there will be movement of individual ridges 102 and valleys 104, the digit taken as a whole needs to be in a substantially fixed position in relation to platen 1 10. Therefore, in step 630, the coupling stability between the digit and the platen is determined.
  • step 635 a decision is made as to whether the coupling is stable enough for pulse rate detection. If not (that is, the digit is in bulk movement in relation to the platen), pulse rate detection ceases and the method returns to step 620 of obtaining a time series of images of the digit. If in step 635, it is determined that the coupling between the digit and the platen 110 is sufficiently stable for pulse rate detection (that is, the digit in bulk is stationary in relation to the platen), then the method continues at step 640.
  • step 640 signal processing is undertaken to process the pressure and/or location fluctuations from one difference frame 551 to the next difference frame 551 , to determine instantaneous fluctuation intensity for each time slice. This method of determining instantaneous fluctuation intensity for each time slice is discussed in further detail below in conjunction with FIG. 7.
  • step 645 a calculation of the pulse rate is made based on the series of instantaneous fluctuation intensities. Step 645 is discussed in further detail below, in conjunction with FIG. 8. In brief, however, a variation of the instantaneous fluctuation intensities is monitored to determine a cyclic pattern in the instantaneous fluctuation intensities over the time series of slices. Based on the pattern of repetition of instantaneous fluctuation intensities, the pulse is determined.
  • Step 645 continues with step 650, which entails displaying, storing, and/or transmitting the pulse rate data.
  • Step 650 either continues with step 620, which is obtaining further time series of images of digits, or step 650 continues with step 655, which entails stopping the method.
  • step 650 It will be understood by persons skilled in the relevant arts that the steps shown as part of method 600 in FIG. 6 are exemplary only. Additional steps may be employed and some steps may be optional. For example, adjusting sensor sensitivity and contrast and/or adjusting frame rate may be optional steps. In addition, it will be understood that while the steps are shown in a particular order, and occurring sequentially, that the order of some steps may be changed, and in addition, some steps may occur in parallel through parallel processing.
  • FIG. 7 is a flowchart of an exemplary method 640 to process pressure/location fluctuations in a fingerprint 106 to determine instantaneous fluctuation intensity for each time slice.
  • FIG. 6 (above) provided an exemplary method 600 for determining a pulse with a fingerprint sensor 200.
  • a time delayed background image subtraction was performed to obtain a difference frame 551 , and by repeating step 625, a series of difference frames 551 is obtained.
  • step 640 of method 600 the difference frames 551 are analyzed to determine a numerical value, known as the "instantaneous fluctuation intensity," for each time slice.
  • FIG. 7 presents in detail the method of step 640.
  • the method 640 begins at step 705.
  • step 710 the difference frame 551 associated with the current time slice (that is, associated with the current frame) is identified.
  • step 715 the pixel grayscale values from the current difference frame 551 are copied to a one dimensional array in memory 408, which may be a temporary array for storage and processing purposes.
  • the pixel values may be copied to a long-term storage, for example hard drive 412 or other long-term storage. The exact order in which the pixels are copied from the current difference frame 551 to the temporary one-dimensional storage array does not matter.
  • step 720 follows.
  • the array of pixel values is sorted from highest to lowest grayscale values.
  • the sorted values may be stored in the same array, overwriting the unsorted values, or may be stored in separate array storage.
  • step 725 the midrange of the grayscale values are deleted from the sorted array.
  • What is retained in the sorted array is a selected percentage of highest and lowest grayscale values in the array. This creates an upper/lower-grayscale-values-array for the difference frame for the current time slice.
  • the values in the upper/lower-grayscale- values-array are the highest pixel values and the lowest pixel values for the current difference frame 551 , and so are representative of the difference between fingerprint image for the current time slice, and the fingerprint image for a time slice from several frames earlier.
  • the retained values in the upper/lower-grayscale- values-array are representative of the difference between the current slice or current fingerprint image, and a slice taken four slices earlier.
  • the highest and lowest grayscale values retained in the upper/lower-grayscale-values-array may as little as two percent of the highest grayscale values and two percent of the lowest grayscale values.
  • the retained percentage may be as much as ten percent of the highest grayscale values, and ten percent of the lowest grayscale values. Higher percentages may be employed as well.
  • step 730 of method 640 a calculation is made as to the standard deviation of the pixel values population of the upper/lower-grayscale-values-array for the current time slice.
  • the standard deviation of the highest and lowest grayscale values is calculated.
  • this standard deviation of the population of the upper/lower-grayscale-values-array is considered to be an "instantaneous fluctuation intensity.”
  • the instantaneous fluctuation intensity is a numerical value embodying or representative of the difference between the fingerprint image of the current slice and the fingerprint image of the preceding time slice that was subtracted from it.
  • the instantaneous fluctuation intensity for the current time slice is inserted into an array structure which is referred to as the "pulse-rate-waveform-array.”
  • the pulse-rate-waveform-array stores or maintains a series of consecutive instantaneous fluctuation intensities, representative of a series of changes in the fingerprint 106 over time.
  • the pulse-rate-waveform-array may be stored in main memory 408, hard disk drive 412, or other secondary memory 410.
  • step 740 of method 640 the next time slice is identified.
  • the method then continues with step 710, which as noted above identifies the difference frame 551 associated with the current time slice.
  • the method 640 continues in this manner, looping from step 710 through step 740, thus populating the pulse-rate- waveform-array with a time series of instantaneous fluctuation intensities for the fingerprint.
  • FIG. 8 is a flowchart of an exemplary method 645 to calculate a pulse rate based upon an array of instantaneous fluctuation intensities.
  • FIG. 6 above included a method step 640 to process pressure/location fluctuations, resulting in difference frames 551 to determine an instantaneous fluctuation intensity for each time slice.
  • Step 640 discussed in greater detail in FIG. 7, yielded a pulse-rate-waveform-array containing a series of instantaneous fluctuation intensities 551.
  • Step 645 entails calculating the pulse rate based on the pulse-rate-waveform-array.
  • the method 645 of FIG. 8 provides further detailed steps to calculate pulse rate according to an exemplary method.
  • Method 645 starts with step 805.
  • step 810 a start-event-timestamp is set which corresponds to the current time.
  • time information and other timestamp information required by the method 645 can be obtained from timer clock 436 of processing system 225, already discussed above in conjunction with FIG. 4.
  • Step 815 follows step 810.
  • the pulse-rate-waveform-array is surveyed to identify, or if necessary update, a maximum value from among all the instantaneous fluctuation intensity values for all the time slices.
  • an end-event-threshold-value is calculated.
  • the end-event-threshold- value is equal to a designated percentage of the peak instantaneous fluctuation intensity identified in step 815.
  • the end-event-threshold- value may be equal to 20% of the maximum instantaneous fluctuation intensity identified in step 815. In an alternative embodiment, some percentage other than 20% may be employed, for example, 10%.
  • step 825 a calculation is made of the current slope of the pulse-rate-waveform
  • the pulse-rate-waveform is a graph or plot resulting from plotting the values in the pulse-rate-waveform-array.
  • the current slope is calculated based on the instantaneous fluctuation intensity in the pulse-rate-waveform-array for the current time slice, and a designated number of the immediately preceding time slices in the pulse-rate-waveform- array.
  • the current time slice and the three preceding time slices may be used to determine the current slope of the pulse-rate-waveform.
  • some other number for example 4, 5, 10 or other values, may be used for the total number of time slices preceding the current time slice, which are used to calculate the current slope. Whatever number of slices are selected, a current slope is calculated for the pulse-rate-waveform-array.
  • step 830 a determination as to whether or not the current slope is greater than the end-event-threshold-value. If not (meaning the current slope is not greater than the end-event-threshold), then the method returns to step 815, which identifies or updates a new peak instantaneous fluctuation intensity among the time slices.
  • step 830 If in step 830, the current slope of the pulse-rate-waveform is greater than the current value of the end-event-threshold, the method continues with step 835, where an end-event-timestamp is set equal to the current time.
  • a time interval is identified based on the difference between the end-event-timestamp and the currently set start-event-timestamp.
  • This time difference, the end-event-time minus the start-event-time may be assigned to a variable known as the rising-edge-elapsed-seconds.
  • the rising-edge-elapsed-seconds is indicative of the time between a most recent pulse of the heart and the immediately preceding pulse of the heart.
  • step 845 a pulse calculation is made based on the rising-edge-elapsed-seconds.
  • the formula for calculating the pulse is:
  • step 850 a determination is made as to whether the beats-per-minutes falls within an allowed range. For example, it is known that the human pulse generally falls in a range between 40 beats-per-minutes and, for example, 120 beats-per-minutes, though persons skilled in the relevant art will recognize other upper and lower bounds may be set. If the pulse (beats-per-minute) that is determined in step 845 is determined to fall outside of the allowed beats-per-minutes range, then the method continues with step 815 of identifying or updating the peak instantaneous fluctuation intensity. [0129] If the beats-per-minutes falls within the allowed range, then in step 855 the beats- per-minutes is added to a buffer of the last several qualified pulse rates (for example, the last five qualified pulse rates).
  • step 860 a new start-event-timestamp is set equal to the current time.
  • the method then returns to step 815, of identifying or updating the peak instantaneous fluctuation intensity among the time slices.
  • the present method for detecting a pulse using a fingerprint sensor depends on the digit of the mammal taken as a whole (that is, in bulk) being substantially stationary relative to platen 1 10.
  • the sensor's pixel grayscales are averaged, grouped by column 310.
  • the averages are copied into an averaged- columns array.
  • the averaged-columns array represents the grayscale profile across the X- axis of the platen 1 10.
  • the averaged-columns array is populated with multiple sequentially averaged column arrays, for multiple time slices.
  • the regression-line of the averaged-columns array is calculated to produce an X-axis slope. If the slope is too far from zero (+/-) then the finger is moving or the coupling between the finger and platen 1 10 is unstable.
  • Plethysmography is a method of measuring variations in the size of an organ or body part on the basis of the amount of blood passing through or present in the part
  • a "plethysmograph” is a device configured to measure variations in the size of an organ or body part on the basis of the amount of blood passing through or present in the part.
  • the exemplary systems disclosed herein may be viewed as plethysmographs, and the exemplary methods disclosed herein may be viewed as methods of plethysmography.
  • the methods disclosed herein for analyzing fingerprint data may be viewed in part or whole as methods of digital signal processing for detemiining a pulse based on fingerprint data, and the processing systems disclosed herein may be viewed in part or whole as digital signal processing systems configured for determining the pulse based on fingerprint data.
  • a background image that is, an older time slices 5XX-N
  • a background image is constantly captured from a (N+l)-frame delay-buffer (for example, a four frame delay buffer), and subtracted from current fingerprint image 5XX to normalize the current slice image to a midlevel number of grayscales (for example, 140 grayscales).
  • the subtraction yields difference frame 551. If a person or other mammal places a digit on the platen 1 10, and no bulk movement or blood-pressure-induced fluctuations take place, then after N slice frames (for example, four frames) the difference frame 551 would appear as a uniform field of pixels at the midlevel grayscale (for example, 140 grayscales).
  • An exemplary target grayscale of 140 is selected so that the grayscale deviations are easier to see on the difference frames 551.
  • the typical peak grayscale fluctuation during pulse-rate acquisition is +/- 30 grayscales (that is, approximately 1 10 to 170 grayscales on a scale of 0 to 255).
  • typically 20 time slices may be taken per second.
  • the background subtraction algorithm effectively creates a high pass filter for every sensor pixel, with a recovery time at 0.200 seconds.
  • This recovery time of 0.200 seconds is relatively short, when compared with pulse rates on the order of beats-per-minute.
  • the relatively short recovery time is indicated due to the quality and stability of the coupling between the mammalian digit and platen 1 10. If a user takes a deep breath, squirms, or shifts position, then the DC offset of the pressure waveform (and the position of the fluctuation zones on platen 1 10) will vary. The skin's pulse-rate-induced pressure fluctuations are fast, causing rapid rise-times, so 0.200 seconds is considered a good compromise between over-attenuation of the pulse signal and pulse-rate signal stability.
  • a first, earlier fingerprint 106 image 5XX-N is compared to a second, later fingerprint 106 image 5XX to obtain a difference between the two images.
  • the comparison is performed by subtracting corresponding pixel values from the first and second fingerprint 106 images.
  • mathematical forms of comparison between two image slices 5XX, 5XX-N other than an operation of subtraction may be used to determine a representative difference value between two time slices or frames of the fingerprint 106.
  • mathematical forms of comparison between two image slices 5XX, 5XX-N may include subtraction of pixel values, but not necessarily subtraction between corresponding pixels of the first frame and second frame.
  • mathematical forms of comparison between two image slices 5XX, 5XX-N may include subtraction of pixel values, but may also include additional operations beyond subtraction to determine the representative difference value between two time slices or frames of the fingerprint 106.
  • multiple calculations may be employed to determine more than one value or a plurality of values, each different calculated value being indicative in a different way of the difference between a give first time slice and a given second time slice.
  • platen 1 10 may be configured to return data descriptive of a fingerprint 106 other than image data of the fingerprint 106. Processing system 225, and processor 404 in particular, may then be configured to determine an alternative difference between time slices of the fingerprint 106 other than a difference between images. Further, processing system 225, and processor 404 in particular, may be configured to determine the pulse of the mammal based on a variation over time in the alternative difference between time slices. CONCLUSION While various embodiments of the present invention have been described above, it should be understood that they have been presented by way of example only, and not limitation.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Physics & Mathematics (AREA)
  • Cardiology (AREA)
  • Biophysics (AREA)
  • Medical Informatics (AREA)
  • Multimedia (AREA)
  • Physiology (AREA)
  • General Physics & Mathematics (AREA)
  • Human Computer Interaction (AREA)
  • Pathology (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Theoretical Computer Science (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Animal Behavior & Ethology (AREA)
  • General Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Veterinary Medicine (AREA)
  • Image Input (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

L'invention concerne un détecteur d'empreintes digitales configuré pour détecter un pouls d'un être humain ou d'un autre mammifère. Une série chronologique d'images d'empreintes digitales est analysée pour identifier des variations, basées sur le temps, de l'emplacement spatial et/ou de la pression à partir des sillons d'un doigt, les variations résultant de variations basées sur le temps du débit sanguin. D'après l'analyse, on détermine si les variations basées sur le temps indiquent un pouls dans un doigt de la personne, et le cas échéant, la fréquence du pouls est déterminée d'après les variations. La sensibilité d'image du doigt sur le détecteur d'empreintes digitales est optimisée pour déterminer le pouls. La série chronologique d'images d'empreintes digitales est également analysée pour déterminer s'il y a un mouvement global du doigt, auquel cas l'analyse de la fréquence du pouls est provisoirement suspendue.
PCT/US2010/060073 2009-12-11 2010-12-13 Détection de la fréquence du pouls au moyen d'un détecteur d'empreintes digitales WO2011072284A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US28579309P 2009-12-11 2009-12-11
US61/285,793 2009-12-11

Publications (1)

Publication Number Publication Date
WO2011072284A1 true WO2011072284A1 (fr) 2011-06-16

Family

ID=44145950

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2010/060073 WO2011072284A1 (fr) 2009-12-11 2010-12-13 Détection de la fréquence du pouls au moyen d'un détecteur d'empreintes digitales

Country Status (2)

Country Link
US (1) US8433110B2 (fr)
WO (1) WO2011072284A1 (fr)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CZ304801B6 (cs) * 2012-10-23 2014-10-29 Vysoké Učení Technické V Brně Způsob detekce živosti v biometrických systémech pomocí bezpečnostního senzoru na základě tepové frekvence
WO2020055569A1 (fr) * 2018-09-13 2020-03-19 Hong Chang Systèmes et procédés d'identification biométrique sécurisée à l'aide d'une pression enregistrée
US11321557B2 (en) 2018-09-11 2022-05-03 Alex C Lee Pressure recording systems and methods for biometric identification

Families Citing this family (67)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1629624B1 (fr) 2003-05-30 2013-03-20 Privaris, Inc. Systeme de securite en-circuit et procedes de commande d'acces a et d'utilisation de donnees sensibles
US7657849B2 (en) 2005-12-23 2010-02-02 Apple Inc. Unlocking a device by performing gestures on an unlock image
KR101961052B1 (ko) 2007-09-24 2019-03-21 애플 인크. 전자 장치 내의 내장형 인증 시스템들
US8528072B2 (en) 2010-07-23 2013-09-03 Apple Inc. Method, apparatus and system for access mode control of a device
US10076920B2 (en) 2011-08-05 2018-09-18 Saeid Mohmedi Card with integrated fingerprint authentication
US9292916B2 (en) * 2011-08-09 2016-03-22 Hid Global Corporation Methods and systems for estimating genetic characteristics from biometric measurements
CN106133748B (zh) 2012-05-18 2020-01-31 苹果公司 用于基于指纹传感器输入来操纵用户界面的设备、方法和图形用户界面
US20140143860A1 (en) * 2012-11-19 2014-05-22 Dotan DRUCKMAN Two tier verification system and method
US10497747B2 (en) 2012-11-28 2019-12-03 Invensense, Inc. Integrated piezoelectric microelectromechanical ultrasound transducer (PMUT) on integrated circuit (IC) for fingerprint sensing
US9114977B2 (en) 2012-11-28 2015-08-25 Invensense, Inc. MEMS device and process for RF and low resistance applications
US9618405B2 (en) 2014-08-06 2017-04-11 Invensense, Inc. Piezoelectric acoustic resonator based sensor
US10726231B2 (en) 2012-11-28 2020-07-28 Invensense, Inc. Integrated piezoelectric microelectromechanical ultrasound transducer (PMUT) on integrated circuit (IC) for fingerprint sensing
US9511994B2 (en) 2012-11-28 2016-12-06 Invensense, Inc. Aluminum nitride (AlN) devices with infrared absorption structural layer
US9888283B2 (en) 2013-03-13 2018-02-06 Nagrastar Llc Systems and methods for performing transport I/O
USD758372S1 (en) * 2013-03-13 2016-06-07 Nagrastar Llc Smart card interface
CN104933770A (zh) * 2014-03-18 2015-09-23 江南大学 一种跑步签到装置
US10216978B2 (en) * 2014-08-26 2019-02-26 Gingy Technology Inc. Fingerprint identification device and fingerprint identification method
US20160246396A1 (en) * 2015-02-20 2016-08-25 Qualcomm Incorporated Interactive touchscreen and sensor array
WO2016165107A1 (fr) * 2015-04-16 2016-10-20 华为技术有限公司 Procédé d'acquisition d'empreinte digitale, appareil d'acquisition d'empreinte digitale et terminal
USD864968S1 (en) 2015-04-30 2019-10-29 Echostar Technologies L.L.C. Smart card interface
US9928398B2 (en) 2015-08-17 2018-03-27 Invensense, Inc. Always-on sensor device for human touch
US20180239979A1 (en) * 2015-09-03 2018-08-23 Nec Corporation Living body recognition device, living body recognition method, and living body recognition program
US9867134B2 (en) 2015-09-30 2018-01-09 Apple Inc. Electronic device generating finger images at a progressively slower capture rate and related methods
WO2017073880A1 (fr) * 2015-10-28 2017-05-04 Lg Electronics Inc. Terminal mobile, et procédé de commande associé
KR20170049280A (ko) 2015-10-28 2017-05-10 엘지전자 주식회사 이동 단말기 및 이의 제어방법
WO2017073874A1 (fr) * 2015-10-28 2017-05-04 Lg Electronics Inc. Terminal mobile
EP3217316A1 (fr) * 2016-03-11 2017-09-13 Nxp B.V. Système et procédé de détection d'empreintes
US10656255B2 (en) 2016-05-04 2020-05-19 Invensense, Inc. Piezoelectric micromachined ultrasonic transducer (PMUT)
US10325915B2 (en) 2016-05-04 2019-06-18 Invensense, Inc. Two-dimensional array of CMOS control elements
US10670716B2 (en) 2016-05-04 2020-06-02 Invensense, Inc. Operating a two-dimensional array of ultrasonic transducers
US10445547B2 (en) 2016-05-04 2019-10-15 Invensense, Inc. Device mountable packaging of ultrasonic transducers
US10315222B2 (en) 2016-05-04 2019-06-11 Invensense, Inc. Two-dimensional array of CMOS control elements
US10562070B2 (en) 2016-05-10 2020-02-18 Invensense, Inc. Receive operation of an ultrasonic sensor
US10706835B2 (en) 2016-05-10 2020-07-07 Invensense, Inc. Transmit beamforming of a two-dimensional array of ultrasonic transducers
US10600403B2 (en) 2016-05-10 2020-03-24 Invensense, Inc. Transmit operation of an ultrasonic sensor
US10632500B2 (en) 2016-05-10 2020-04-28 Invensense, Inc. Ultrasonic transducer with a non-uniform membrane
US10441975B2 (en) 2016-05-10 2019-10-15 Invensense, Inc. Supplemental sensor modes and systems for ultrasonic transducers
US10452887B2 (en) 2016-05-10 2019-10-22 Invensense, Inc. Operating a fingerprint sensor comprised of ultrasonic transducers
US10408797B2 (en) 2016-05-10 2019-09-10 Invensense, Inc. Sensing device with a temperature sensor
US11673165B2 (en) 2016-05-10 2023-06-13 Invensense, Inc. Ultrasonic transducer operable in a surface acoustic wave (SAW) mode
US10539539B2 (en) 2016-05-10 2020-01-21 Invensense, Inc. Operation of an ultrasonic sensor
KR102028269B1 (ko) * 2017-05-03 2019-11-04 선전 구딕스 테크놀로지 컴퍼니, 리미티드 바이털 사인 확정 방법, 신분 인증 방법 및 그의 장치
US10891461B2 (en) 2017-05-22 2021-01-12 Invensense, Inc. Live fingerprint detection utilizing an integrated ultrasound and infrared sensor
US10474862B2 (en) 2017-06-01 2019-11-12 Invensense, Inc. Image generation in an electronic device using ultrasonic transducers
DE102018114186A1 (de) 2017-06-15 2018-12-20 Egis Technology Inc. Optischer Fingerabdrucksensor und Verfahren zum Herstellen eines entsprechenden Erfassungsmoduls dafür
US20180373393A1 (en) * 2017-06-26 2018-12-27 Qualcomm Incorporated Methods and Apparatuses for Detecting Touch Motion with Ultrasonic Sensors
US10643052B2 (en) 2017-06-28 2020-05-05 Invensense, Inc. Image generation in an electronic device using ultrasonic transducers
CN107368221B (zh) * 2017-07-21 2020-07-10 北京小米移动软件有限公司 压力确定方法和装置、指纹识别方法和装置
US10997388B2 (en) 2017-12-01 2021-05-04 Invensense, Inc. Darkfield contamination detection
US10984209B2 (en) 2017-12-01 2021-04-20 Invensense, Inc. Darkfield modeling
WO2019109010A1 (fr) 2017-12-01 2019-06-06 Invensense, Inc. Suivi de fond noir
US11151355B2 (en) 2018-01-24 2021-10-19 Invensense, Inc. Generation of an estimated fingerprint
KR102517692B1 (ko) * 2018-02-05 2023-04-03 삼성전자주식회사 혈압 측정 장치 및 방법
US10755067B2 (en) 2018-03-22 2020-08-25 Invensense, Inc. Operating a fingerprint sensor comprised of ultrasonic transducers
KR102592077B1 (ko) * 2018-08-01 2023-10-19 삼성전자주식회사 생체정보 측정 장치 및 방법
US10936843B2 (en) 2018-12-28 2021-03-02 Invensense, Inc. Segmented image acquisition
US11188735B2 (en) 2019-06-24 2021-11-30 Invensense, Inc. Fake finger detection using ridge features
US11216681B2 (en) 2019-06-25 2022-01-04 Invensense, Inc. Fake finger detection based on transient features
US11216632B2 (en) 2019-07-17 2022-01-04 Invensense, Inc. Ultrasonic fingerprint sensor with a contact layer of non-uniform thickness
US11176345B2 (en) 2019-07-17 2021-11-16 Invensense, Inc. Ultrasonic fingerprint sensor with a contact layer of non-uniform thickness
US11232549B2 (en) 2019-08-23 2022-01-25 Invensense, Inc. Adapting a quality threshold for a fingerprint image
US11392789B2 (en) 2019-10-21 2022-07-19 Invensense, Inc. Fingerprint authentication using a synthetic enrollment image
CN111339896B (zh) * 2020-02-21 2021-09-24 武汉华星光电技术有限公司 一种指纹识别结构及显示面板
CN115551650A (zh) 2020-03-09 2022-12-30 应美盛公司 具有非均匀厚度的接触层的超声指纹传感器
US11243300B2 (en) 2020-03-10 2022-02-08 Invensense, Inc. Operating a fingerprint sensor comprised of ultrasonic transducers and a presence sensor
US11328165B2 (en) 2020-04-24 2022-05-10 Invensense, Inc. Pressure-based activation of fingerprint spoof detection
US11995909B2 (en) 2020-07-17 2024-05-28 Tdk Corporation Multipath reflection correction

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5978495A (en) * 1996-07-17 1999-11-02 Intelnet Inc. Method and apparatus for accurate determination of the identity of human beings
US20060204062A1 (en) * 2001-12-04 2006-09-14 Kazuyuki Shigeta Image input apparatus, subject identification system, subject verification system and image input method
US20080273768A1 (en) * 2007-05-04 2008-11-06 Stmicroelectronics (Research & Development) Limited Biometric sensor apparatus and method
US20090069668A1 (en) * 2007-09-03 2009-03-12 Alto Stemmer Method and magnetic resonance system to optimize mr images
US20090092290A1 (en) * 2004-06-01 2009-04-09 Lumidigm, Inc. Multispectral Imaging Biometrics

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2749955B1 (fr) * 1996-06-14 1998-09-11 Thomson Csf Systeme de lecture d'empreintes digitales
JP3858263B2 (ja) * 2001-11-09 2006-12-13 日本電気株式会社 指紋画像入力装置及びそれを用いた電子機器
US7844083B2 (en) * 2004-06-18 2010-11-30 Kyushu Institute Of Technology Method for acquiring personal identification data, personal identification method, apparatus for acquiring personal identification data, and personal identification apparatus

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5978495A (en) * 1996-07-17 1999-11-02 Intelnet Inc. Method and apparatus for accurate determination of the identity of human beings
US20060204062A1 (en) * 2001-12-04 2006-09-14 Kazuyuki Shigeta Image input apparatus, subject identification system, subject verification system and image input method
US20090092290A1 (en) * 2004-06-01 2009-04-09 Lumidigm, Inc. Multispectral Imaging Biometrics
US20080273768A1 (en) * 2007-05-04 2008-11-06 Stmicroelectronics (Research & Development) Limited Biometric sensor apparatus and method
US20090069668A1 (en) * 2007-09-03 2009-03-12 Alto Stemmer Method and magnetic resonance system to optimize mr images

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CZ304801B6 (cs) * 2012-10-23 2014-10-29 Vysoké Učení Technické V Brně Způsob detekce živosti v biometrických systémech pomocí bezpečnostního senzoru na základě tepové frekvence
US11321557B2 (en) 2018-09-11 2022-05-03 Alex C Lee Pressure recording systems and methods for biometric identification
WO2020055569A1 (fr) * 2018-09-13 2020-03-19 Hong Chang Systèmes et procédés d'identification biométrique sécurisée à l'aide d'une pression enregistrée
US11281755B2 (en) 2018-09-13 2022-03-22 Hong Chang Systems and methods for secure biometric identification using recorded pressure

Also Published As

Publication number Publication date
US8433110B2 (en) 2013-04-30
US20110170750A1 (en) 2011-07-14

Similar Documents

Publication Publication Date Title
US8433110B2 (en) Pulse-rate detection using a fingerprint sensor
CN104054038B (zh) 个体鉴别装置和个体鉴别方法
CN108882872A (zh) 生物体信息分析装置、生物体信息分析系统、程序以及生物体信息分析方法
US8805019B2 (en) Processing images of at least one living being
CN109044314B (zh) 一种基于欧拉视频放大的非接触式心率监测方法
CN105266772A (zh) 一种生理参数的测量方法
US10750988B2 (en) Fatigue degree determination device, and fatigue degree determination method
EP3241492B1 (fr) Procédé et dispositif de détection de fréquence cardiaque
EP2747649A1 (fr) Procédé et appareil pour surveiller le réflexe de barorécepteur d'un utilisateur
CN104665803B (zh) 基于智能平台的检测房颤系统
CN109452935A (zh) 使用统计后处理从血管容积图估计血压的无创方法和系统
Sun et al. Assessment of photoplethysmogram signal quality using morphology integrated with temporal information approach
US20230157647A1 (en) Computer-implemented method for synchronizing a photoplethysmography (ppg) signal with an electrocardiogram (ecg) signal
CN108717872A (zh) 基于面部、手部识别和大数据的健康分析方法及系统
CN110457981B (zh) 活体侦测的方法、装置及电子装置
CN110418603A (zh) 血压数据处理装置、血压数据处理方法以及程序
Pal et al. Improved heart rate detection using smart phone
CN118338853A (zh) 计算机程序、信息处理方法及信息处理装置
CN114515147B (zh) 一种基于bcg信号与ppg信号融合的生理监测系统
US20220386886A1 (en) Non-contact heart rhythm category monitoring system and method
Pansare et al. Heart Rate Measurement from Face and Wrist Video
Popescu et al. Cardiowatch: A solution for monitoring the heart rate on a Mobile device
US20210235998A1 (en) Method and Apparatus for Determining the Impact of Behavior-Influenced Activities on the Health Level of a User
Nair et al. Non-contact heart-rate measurement using KLT-algorithm
KR102570982B1 (ko) 비접촉 생체정보 측정 방법

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10836798

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 10836798

Country of ref document: EP

Kind code of ref document: A1