Feature authentication using multi-view image sensor

Abstract

This document describes systems and techniques aimed at topographic feature authentication using a multi-view image sensor. A computing device (102) having an image sensor (104) and a biometric authentication manager (108) is configured to capture images representing topographic features of an object from two or more perspectives. Based on the image (104), the biometric authentication manager (108) determines a topographic feature of the object and compares the determined topographic feature to a previously captured topographic feature to determine whether the object and a previously imaged object are the same object. In response to determining that the object is the same object as a previously imaged object, the biometric authentication manager (108) changes rights to a resource associated with the computing device (102). By performing the techniques and systems described above, a biometric authentication manager (108) is effective in reusing an image sensor (104) as a biometric authentication scanner.

Description

Background Art

[0001] Dedicated biometric authentication systems (e.g., facial authentication systems, fingerprint authentication systems) are seamless, sophisticated methods for authenticating users of computing devices (e.g., smartphones, laptops). These systems provide secure, convenient user authentication for users, enabling users to change permissions for resources associated with the computing device (e.g., unlock the device, unlock applications on the device). However, implementing one of these dedicated biometric authentication systems in a computing device may require specific hardware, space within the housing of the computing device, and special materials.

[0002] For example, a fingerprint authentication system utilizing an optical under-display fingerprint scanner (UDFPS) may require a display material that becomes translucent during fingerprint scanning or a transparent adhesive that bonds the UDFPS to the bottom surface of the display. In another example, in order to allocate space for the UDFPS within the housing of the computing device, the housing may need to be enlarged or the battery of the computing device may need to be reduced. This may compromise the intended product design of the computing device (e.g., the thinness of a smartphone) by increasing the physical size and weight of the computing device, making the computing device unwieldy for the user. This may also reduce the battery life of the computing device, making the computing device unreliable for all-day use. As another example, a facial authentication system may utilize one or more cameras, dot projectors, and infrared sensors to achieve low-light functionality. These hardware modules may require space in the front of the housing of the computing device. If the manufacturer desires to maintain a large display, the manufacturer may allocate space above the display, which, in addition to reducing the screen-to-body ratio, also increases the size and weight of the computing device. Alternatively, the manufacturer may allocate space in the notch of the display, but this will sacrifice a uniform, uninterrupted display. Summary of the invention

[0003] This document describes systems and techniques for topographic authentication using a multi-view image sensor. A computing device having an image sensor and a biometric authentication manager is configured to capture images representing topographic features of an object from two or more angles. Based on the images, the biometric authentication manager determines topographic features of the object and compares the determined topographic features with previously captured topographic features to determine whether the object and the previously imaged object are the same object. In response to determining that the object and the previously imaged object are the same object, the biometric authentication manager changes permissions on resources associated with the computing device.

[0004] In various aspects, a method for topographical feature authentication using a multi-view image sensor is disclosed. The method includes capturing one or more images, the one or more images representing the topographical features of an object from two or more perspectives, the one or more images being captured by one or more image sensors having two or more image capture perspectives. For example, one or more image sensors may be incorporated into the back of a housing of a computing device as part of one or more cameras. Two or more image capture perspectives may be possible by using a sensor with an appropriate pixel count and pixel pitch configured to provide two or more image capture perspectives or two sensors physically separated from each other. The method also includes determining the topographical features of an object based on the one or more images. In an example, the object may be a fingertip, a palm, or another appropriate biometric identifier. The topographical features may be details of a fingerprint, lines in a palm, or other unique physical features of another biometric identifier. In addition, the method includes comparing the topographical features of the object with previously captured topographical features of a previously imaged object to provide a comparison result. The method also includes determining that the object is the same object as the previously imaged object based on the comparison result and reaching a threshold confidence level. In response to determining that the object is the same object as the previously imaged object, the method includes changing permissions on a resource associated with the computing device. For example, changing permissions may include unlocking the computing device so that the user can access functions and features of the computing device's operating system. Changing permissions may also include unlocking resources or applications associated with the computing device, assuming the device is already in an unlocked state.

[0005] In an additional aspect, a computing device is disclosed. The computing device includes one or more image sensors, one or more processors, and a memory. The memory stores instructions that, when executed by the one or more processors, cause the one or more processors to implement a biometric authentication manager responsible for repurposing the one or more image sensors as a biometric authentication scanner by performing the methods described herein.

[0006] The details of one or more implementations are set forth in the accompanying drawings and in the following detailed description. Other features and advantages will be apparent from the detailed description, drawings, and claims. This Summary is provided to introduce the subject matter that is further described in the detailed description. Therefore, the reader should neither regard this Summary as describing essential features nor as limiting the scope of the claimed subject matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Details of one or more aspects of repurposing one or more image sensors as fingerprint authentication scanners are described in this document with reference to the following figures, where the use of the same numerals in different instances may indicate similar features or components:

[0008] Figure 1 An example implementation of an example computing device having one or more image sensors, one or more processors, memory, and a biometric authentication manager is shown;

[0009] Figure 2 A biometric authentication scanner configured to use one or more image sensors as a biometric authentication scanner is shown. Figure 1 Example implementations of example computing devices;

[0010] Figure 3 In more detail, the Figure 2 Example implementations of a computing device;

[0011] Figure 4 An example implementation of a Bayer color filter array is shown;

[0012] Figure 5 An example implementation of a dual pixel auto focus (DPAF) image sensor is shown;

[0013] Figure 6 An example implementation of an example computing device configured to use one or more image sensors as a biometric authentication scanner is shown;

[0014] Figure 7 Shown from Figure 6 An example operating environment for a user of a computing device; and

[0015] Figure 8 A method of using an image sensor as a biometric authentication scanner is described. DETAILED DESCRIPTION

[0016] Overview

[0017] Many computing devices (e.g., smartphones, tablets, personal computers) include dedicated biometric authentication systems. The dedicated biometric authentication system may be a facial authentication system, a fingerprint authentication system, etc. Depending on the intended product design, the manufacturer of the computing device may choose one dedicated biometric authentication method over another. For example, if the intended product design is a notchless display, the manufacturer may choose to incorporate a fingerprint scanner on the front of the housing of the computing device below the notchless display. In another example, and in addition to a notchless display, if the intended product design is to maximize the screen-to-body ratio of the computing device, the manufacturer may choose to incorporate an optical UDFPS below a portion of the notchless display.

[0018] However, including a dedicated biometric authentication system in a computing device may require specific hardware modules, space within the housing of the computing device, and special materials. For example, a fingerprint authentication system utilizing an optical UDFPS may require a display material that becomes translucent during a fingerprint scan or a transparent adhesive that bonds the UDFPS to the bottom surface of the display. As an additional example, a facial authentication system may be designed to work in low light situations by incorporating an infrared camera module in the front of the housing of the computing device. Manufacturers may implement a dedicated biometric authentication system by incorporating the necessary hardware modules or technologies from third-party suppliers, as in the examples mentioned above. Alternatively, manufacturers may develop first-party hardware modules or technologies to incorporate. The examples provided may increase the bill of materials for the computing device, which may ultimately increase the consumer cost of the computing device for the user. In addition, depending on the hardware modules or technologies incorporated, the expected product design of the computing device (e.g., thinness, notch-free display, all-day battery life) may be compromised.

[0019] Compromised product design, increased physical size and weight, shorter battery life, or increased consumer cost are a few examples of how including a dedicated biometric authentication system can result in a poor user experience. This document describes systems and techniques for topographic authentication using multi-view image sensors, thereby avoiding the compromised product design, increased physical size and weight, shorter battery life, or increased consumer cost typically associated with dedicated biometric authentication systems.

[0020] The following discussion describes an operating environment, techniques that may be employed in the operating environment, and example methods. Although systems and techniques for repurposing one or more image sensors as fingerprint authentication scanners are described, it should be understood that the subject matter of the appended claims is not necessarily limited to the specific features or methods described. Rather, specific features and methods are disclosed as example implementations and with reference to the operating environment by way of example only.

[0021] Example Device

[0022] Figure 1An example implementation 100 of an example computing device 102 is shown having one or more image sensors 104, a display 106, and a biometric authentication manager 108 configured to repurpose the image sensor 104 as a biometric authentication scanner. In this implementation, the computing device 102 is a budget smartphone that does not include a dedicated biometric authentication system. The image sensor 104 can be a variety of image sensors, including a monochrome image sensor, a Bayer filter image sensor, a phase detection autofocus sensor, a dual pixel autofocus sensor, etc. The image sensor 104 is integrated into the back of the housing of the computing device 102 as part of a camera. In front of the image sensor 104 is a camera lens assembly and a camera lens cover (e.g., glass, plastic). The display 106 is integrated as part of the front of the housing of the computing device 102.

[0023] In the example, user 110 wishes to interact with computing device 102. User 110 picks up computing device 102, which is in locked state 112-1, as shown in display 106-1. In response to user 110 picking up computing device 102, which is in locked state 112-1, biometric authentication manager 108 displays prompt 114 to place a finger on the camera lens cover. User 110 places a finger on the camera lens cover, at which point biometric authentication manager 108 instructs the camera to capture one or more images using image sensor 104. In this example, the one or more images represent topographical features of a fingerprint of the finger. However, an object having topographical features may be any object suitable for biometric authentication, including a palm, thumb, etc. Based on the one or more images, biometric authentication manager 108 determines the topographical features of the fingerprint. For example, the topographical features may include details of the ridges, valleys, and patterns of the fingerprint.

[0024] In response to determining the topographical features of the fingerprint, the biometric authentication manager 108 compares the topographical features to previously captured topographical features of a previously imaged object to provide a comparison result. The previously imaged object may be the same fingerprint of the user 110, for example, an image of the fingerprint may have been captured during an initial setup process of the computing device 102. Based on the comparison results, the biometric authentication manager 108 determines that the fingerprint of the user 110 and the previously imaged object are the same object. In examples where the threshold confidence level is not met, the biometric authentication manager 108 may display an error message detailing that the fingerprint and the previously imaged object are not the same object.

[0025] In response to determining that the fingerprint and the previously imaged object are the same fingerprint of the user 110, the biometric authentication manager 108 changes permissions (e.g., unlocks) to resources (e.g., an operating system) associated with the computing device 102. By executing the methods detailed above, the biometric authentication manager 108 enables a biometric authentication scanner that the user 110 can use to authenticate himself, thereby changing permissions to resources associated with the computing device 102.

[0026] For example, in the absence of a biometric authentication scanner enabled by the biometric authentication manager 108, the user 110 may need to authenticate himself in a different way, such as entering a password, a pin, a pattern, or another non-biometric method. These alternatives allow for errors (such as entering an incorrect password) as well as security issues (such as a malicious user obtaining and entering the correct password). In many cases, entering an incorrect password is a simple inconvenience for the user 110, and the user can simply enter the password again in the hope that it is correct. However, if the user 110 enters an incorrect password multiple times (e.g., 10 times, 20 times), depending on the operating system of the computing device 102, the operating system may prohibit the user 110 from unlocking the computing device 102 for a period of time (e.g., a few seconds, a few minutes, a few hours). This can be frustrating for the user 110. On the other hand, security issues are critical for many users of computing devices. For example, suppose that the user 110 has previously used the computing device 102 to open an application containing sensitive personal information (e.g., financial information, health information), and the application is still open on the computing device 102. If a malicious user gains access to computing device 102, the malicious user can obtain and use sensitive personal information to negatively impact user 110 (e.g., steal their identity, steal their money). This is not only a serious problem for user 110, but is also another example of a compromised user experience. However, this and other issues are mitigated for user 110 having a computing device 102 that has a biometric authentication manager 108 configured to repurpose one or more image sensors 104 as a biometric authentication scanner.

[0027] In more detail, Figure 2 Shown from Figure 1An example implementation 200 of a computing device 102 configured to use one or more image sensors 104 as a biometric authentication scanner (e.g., repurposing an existing camera as a biometric authentication scanner). The computing device 102 is shown as a variety of example devices, including consumer electronic devices. As non-limiting examples, the computing device 102 can be a smart phone 102-1, a tablet computer 102-2, a laptop computer 102-3, a desktop all-in-one computer 102-4, a smart watch 102-5, a pair of smart glasses 102-6, a game controller 102-7, a smart home speaker 102-8, and a microwave appliance 102-9. Although not shown, the computing device 102 can also be an automated teller machine, an audio / video recording device, a health monitoring device, a home automation system, a home security system, a game console, a drone, a home appliance, etc. It should be noted that the computing device 102 can be wearable, non-wearable but mobile (mobile), or relatively non-mobile (e.g., a desktop computer, a home appliance). It should also be noted that the computing device 102 can be used with or embedded within many computing devices 102 or peripherals, such as in an attachment to a motor vehicle or a personal computer. The computing device 102 may include a plurality of computing devices 102 and peripherals, such as ... Figure 2 Omitted add-ons and interfaces.

[0028] As shown, the computing device 102 includes an image sensor 104, one or more processors 202, and a computer readable medium 204 (CRM 204). The image sensor 104 may include one or more of any suitable image sensor (e.g., a phase detection autofocus sensor, a dual pixel autofocus (DPAF) sensor, a charge coupled device sensor). The processor 202 may include one or more of any suitable single-core or multi-core processor (e.g., a central processing unit, a graphics processing unit). The CRM 204 includes a memory medium 206 and a storage medium 208. An operating system 210 (OS 210), applications 212, and a biometric authentication manager 108-1 are implemented as computer readable instructions on the CRM 204. These computer readable instructions may be executed by the processor 202 to provide some or all of the functionality described herein. For example, the processor 202 may perform specific computing tasks of the OS 210 intended to repurpose the image sensor 104 as a biometric authentication scanner. The CRM 204 may include one or more non-transitory storage devices, such as a solid-state drive, a magnetic rotating drive, a random access memory, or any type of memory medium suitable for storing electronic instructions, each coupled to a data bus. The term "coupled" may refer to two or more elements that are in direct contact (physically, electrically, optically, etc.), or to two or more elements that are not in direct contact with each other but still cooperate or interact with each other.

[0029] In various aspects, various implementations of the biometric authentication manager 108-1 may include one or more integrated circuits, systems on a chip, secure key storage, hardware embedded with firmware stored on a read-only memory, a printed circuit board with various hardware components, or any combination thereof. As described herein, the biometric authentication manager 108-1 may include one or more components of the computing device 102 that are configured to repurpose the image sensor 104 as a biometric authentication scanner. In additional aspects, the biometric authentication manager 108-1 may be implemented as the computing device 102.

[0030] As shown, the computing device 102 also includes input / output ports 214 (I / O 214). The I / O ports 214 enable the computing device 102 to interact with other computing devices or users through peripheral devices, thereby sending any combination of digital, analog, and radio frequency signals. The I / O ports 214 may include any combination of internal or external ports, such as a universal serial bus (USB) port, an audio port, a video port, a dual in-line memory module card slot, a peripheral component interconnect express slot, etc. Various peripheral devices can be operatively coupled to the I / O ports 214, such as a human input device, an external CRM, a speaker, a display, or other peripheral devices.

[0031] The computing device 102 also includes a communication system 216 that enables the transmission of device data such as received data, transmitted data, or other data as described herein, and can provide connections to one or more networks and other devices connected thereto. For example, the communication system 216 includes a near field communication transceiver, a wireless local area network (WLAN) radio, a wireless wide area network (WWAN) radio, and an infrared transceiver. Depending on the communication protocol or standard in accordance with which the computing device 102 communicates, the data transmitted through the communication system 216 can be packetized or framed. The communication system 216 can include a wired interface that facilitates communication through a local network, a private network, an intranet, or the Internet, such as an optical fiber or Ethernet interface. The communication system 216 can also include a wireless interface that facilitates communication through a WLAN, a WWAN, or a cellular network, such as a Wi-Fi and Bluetooth interface.

[0032] Although not shown, the computing device 102 may include a system bus, interconnect, or data transfer system that couples the various components within the computing device 102. The system bus, interconnect, or data transfer system may include any one or combination of a variety of bus structures such as a memory bus, a peripheral bus, a USB, and / or a local bus utilizing any of a variety of bus architectures.

[0033] In addition, the computing device includes a display 106 (e.g., a touch screen display, a liquid crystal display (LCD)). The computing device may include or utilize any of a variety of displays, including an in-plane switching LCD, a vertically aligned LCD, an organic light emitting diode (OLED) display, etc. The display may be referred to as a screen, so that content (e.g., images, videos) can be displayed on the screen.

[0034] Figure 3 An example implementation 300 of a computing device 102 having one or more image sensors 104 is shown in greater detail. As shown in detail view 300-1, computing device 102 includes a camera 302 on a back side of a housing of computing device 102. One or more image sensors 104 are incorporated into camera 302.

[0035] Detailed view 300-2 shows a cross-sectional view of camera 302. For clarity of the detailed view, some components of camera 302 may be omitted. As shown, camera 302 includes a transparent cover layer 304 (e.g., glass, plastic) disposed as a top layer. A lens assembly 306 consisting of a plurality of lenses 308 is disposed below cover layer 304. A microlens array 310 is disposed below the lens assembly. A color filter array 312 (CFA 312) including a plurality of color filter elements 314 is disposed below the microlens array 310. One or more image sensors 104 are disposed below CFA 312. In this example implementation 300, one or more image sensors 104 are incorporated into camera 302 as a single image sensor 104-1. Image sensor 104-1 includes a plurality of photodiodes 316 that can detect the intensity of light but not the color of light.

[0036] Although not shown, lens assembly 306 may include a motor operably coupled (e.g., physically, magnetically) to one or more of lenses 308. For example, as part of an autofocus system, the motor may displace one or more of lenses 308 to appropriately focus light onto image sensor 104-1. Lens assembly 306 and microlens array 310 focus the light onto a photosensitive area of ​​image sensor 104-1. Depending on the application of camera 302, CFA 312 may be one of a variety of suitable CFAs, including a red-yellow-yellow-blue CFA, a cyan-yellow-yellow-magenta CFA, a Bayer CFA, etc. CFA 312 filters the intensity of light by wavelength range so that the individual filtered intensities detected by photodiode 316 include color information.

[0037] Figure 4 The implementation of the Bayer CFA 312-1 from Figure 34. An example implementation 400 of a CFA 312 of a Bayer CFA 312 is shown. As shown in a top-down view, the Bayer CFA 312-1 is a six-by-six square grid of square color filter elements 314. Although a square grid of square color filter elements is shown, the shape of the grid and the shape of the color filter elements 314 can be any suitable shape (e.g., rectangular). In addition, the size of the square grid can be any suitable size (e.g., 1,000 by 1,000, 1,920 by 1,080). As also shown, the color filter elements 314 can be one of three different color filter elements 314 that are effective in filtering the intensity of light by wavelength range. In this example, the color filter elements 314 are divided into green color filter elements 314-1, blue color filter elements 314-2, and red color filter elements 314-3. Each two-by-two grid of color filter elements 314 includes two green color filter elements 314-1, one blue color filter element 314-2, and one red color filter element 314-3. This two-by-two grid, outlined in bold, may be referred to as pixels 402 .

[0038] Figure 5 The DPAF sensor 104-2 is implemented as a dual pixel auto focus sensor 104-2 (DPAF sensor 104-2). Figure 3 1 is an example implementation 500 of an image sensor 104-1 of FIG. 1 . As shown in a top-down view, the DPAF sensor 104-2 is composed of an array of dual pixels 502, each of which is indicated by a square with a solid outline. The array of dual pixels 502 is arranged in a six by six square grid. Although a six by six square grid grid is shown, the grid of dual pixels 502 can be any suitable size (e.g., 1,000 by 1,000) and shape (e.g., rectangular). As shown, the dual pixels 502 are split into a left photodiode 504 and a right photodiode 506.

[0039] In a conventional application of the DPAF sensor 104-1, light is focused by the microlens array 310 onto the left photodiode 504 and the right photodiode 506. Before capturing an image of the object, the photodiode amplitude difference between the left photodiode 504 and the right photodiode 408 is calculated. The photodiode amplitude difference can be referred to as the phase of the dual pixel 502. If the phase is not equal to or close to zero within a threshold confidence level, one or more of the lenses 308 operably coupled to the motor can be positioned, for example, by a motor, until the phase is equal to or close to zero within a threshold confidence level. Once the phase is equal to or close to zero within a threshold confidence level, the object of the image is in focus. When capturing an image of the object, the photodiode amplitudes of the left photodiode 504 and the right photodiode 506 are combined to record a complete dual pixel.

[0040] However, the systems and techniques described herein for topographical feature authentication using a multi-view image sensor take the opposite approach. By positioning one or more lenses 308 (e.g., by a motor) such that the object of the image is out of focus, the phase of the dual pixel 502 is changed (e.g., maximized). The document now turns to an example operating environment in which the maximized phase is used to enable the DPAF sensor 104-2 to be repurposed as a biometric authentication scanner.

[0041] Example operating environment

[0042] Figure 6 An example implementation 600 is shown of a user 602 of a computing device 604. The computing device 604 includes a display 606, a processor 608, a camera 610, and a biometric authentication manager 108 configured to use one or more image sensors as a biometric authentication scanner. The camera 610 includes a DPAF sensor 612 and an LED flash 614.

[0043] Figure 7 An example operating environment 700 of a user 602 of a computing device 604 is shown. In this example, the user 602 picks up the computing device 604, which is in a locked state 702 as indicated by the display 606-1. At 700-1, in response to the user 602 picking up the computing device 604 and tapping the display 606-1, the biometric authentication manager 108 displays to the user 602 a prompt 704 for applying a user input to an overlay of the camera 610. At 700-2, the biometric authentication manager 108 may recognize the user input, in this example a fingertip 706 with a fingerprint, through a proximity sensor or another suitable sensor of the computing device 604. In another example, the biometric authentication manager 108 may recognize the fingertip 706 by determining (e.g., through light levels) that the camera 610 is being obscured by the fingertip 706. Also at 700-2, in response to determining that the fingertip 706 is blocking the camera 610, the biometric authentication manager 108 can instruct the camera 610 (e.g., by positioning one or more lenses via a motor) to achieve a focus corresponding to the out-of-focus image. For example, when the object of the image (the fingertip 706) rests less than one centimeter above the cover of the camera 610, the focus can be adjusted to infinity. By doing so, the phase of each dual pixel 502 in the DPAF sensor 612 can be maximized.

[0044] Additionally, at 700-2, the biometric authentication manager 108 may instruct the camera 610 to activate the LED flash 614 to illuminate the fingertip 706 to increase the signal-to-noise ratio. In some examples, the LED flash 614 may be positioned in the camera 610 so that the fingertip 706 is illuminated in a direction sufficient to more substantially alter or emphasize fingerprint features from the first perspective than the fingerprint features from the second perspective, thereby facilitating identification of the features. In this example, the left photodiode 504 and the right photodiode 506 of each dual pixel 502 of the DPAF sensor 612 observe the first perspective and the second perspective, respectively. The alteration or emphasis of features of the fingerprint may enable greater resolution of those features.

[0045] Also at 700-2, the biometric authentication manager 108 may instruct the camera 610 to capture two image data of the fingerprint (one data from the left photodiode 504 and one data from the right photodiode 506), rather than combining the photodiode amplitudes of the left photodiode and the right photodiode of each dual pixel 502. At 700-3, the biometric authentication manager 108 may instruct the processor 608 to calculate a disparity map that records the phase of each dual pixel photodiode pair in the image data captured at 700-2. The biometric authentication manager 108 may input the disparity map into a custom normalization function to generate an embedding 708 (e.g., embedding 708-1) or other suitable representation of the image data (e.g., a hash code, a string of numbers) that does not include any visual information. At 700-4, the final embedding 710 of the fingerprint of the user 602 may be generated in multiple steps, thereby compressing the image data captured at 700-2 and 700-3 at each step.

[0046] At 700-5, the biometric authentication manager 108 may instruct the processor 202 to compare the final embedding 710 of the fingerprint to the embedding library 712. During the initial setup process of the computing device 604, the biometric authentication manager 108 may generate the embedding library 712 by prompting the user 602 to scan the fingerprint at slightly different locations multiple times, calculating the final embedding for each scan, and storing the final embedding on the memory of the computing device 604. At 700-6, in response to the comparison, the biometric authentication manager 108 may provide a comparison result 714. The biometric authentication manager 108 may determine that the final embedding 710 of the fingerprint and the fingerprint used to generate the embedding library 712 are the same fingerprint based on the comparison result 714 and the reaching of the threshold confidence level. At 700-7, in response to determining that the final embedding 710 of the fingerprint and the embedding library 712 of the fingerprint belong to the same fingerprint, the biometric authentication manager 108 transitions the computing device from the locked state 702 to the unlocked state 716, as shown in the display 606-2.

[0047] In an implementation, the computing device 604 may be in an unlocked state 716, but a high-right resource (e.g., a financial application) of the computing device 604 that requires access rights may be in a locked state. In such implementations, the biometric authentication manager 108 may perform the operations described herein to unlock the high-right resource. In other implementations, the computing device 604 may be in an unlocked state, the high-right resource may be in a locked state, and the computing device 604 may include a dedicated biometric authentication system, such as a fingerprint scanner on the front of the computing device 604. In these implementations, the high-right resource may require two-factor authentication (2FA) to unlock. The biometric authentication manager 108 may perform the operations described herein as one of the two factors of 2FA, the other factor being, for example, a fingerprint scanner on the front of the computing device 604. Alternatively or in addition, the biometric authentication manager 108 may perform the described operations simultaneously with the scanning of a second finger by the fingerprint scanner on the front of the computing device 604. In another implementation, the computing device 604 may be in a locked state, requiring 2FA to unlock from the locked state, and includes a dedicated biometric authentication system, such as a facial scanner on the front of the computing device 604. The biometric authentication manager 108 may perform the operations described herein as one of the two factors of 2FA, the other factor being the facial scanner on the front of the computing device 604. The biometric authentication manager 108 may, for example, perform operations simultaneously or sequentially with the scanning of the face by the facial scanner on the front of the computing device 604. Although a facial scanner on the front of the computing device 604 is described, the dedicated biometric authentication system may be any suitable system (e.g., UDFPS, capacitive fingerprint scanner, optical facial scanner) on any suitable side (e.g., front, back) of the computing device 604. For example, the biometric authentication manager 108 may use a front or rear camera as one of the two factors of 2FA to perform the methods described herein. At the same time, a dedicated capacitive fingerprint scanner on the opposite side or the same side of the computing device 604 may scan different fingertips of the user as another factor of 2FA.

[0048] Although a single DPAF sensor is described, any one or more of suitable image sensors configured to provide two or more image capture angles of view may be utilized. For example, two image sensors are physically separated from each other and configured to provide two or more image capture angles of view that can be utilized. In another example, a single non-DPAF image sensor configured to provide two or more image capture angles of view may be utilized.

[0049] Example Method

[0050] Figure 8A method 800 is outlined that enables the use of an image sensor as a biometric authentication scanner. The method is shown as a set of boxes that specify the operations performed. The method is not necessarily limited to the order or combination of the set of boxes shown for performing the operations as the corresponding boxes. In addition, any one or more of the operations may be repeated, combined, reorganized, or concatenated to provide a number of additional or alternative methods. In portions of the following discussion, reference may be made to Figure 1 Example implementation of Figures 2 to 7 Entities detailed in the , references to which are made are made only as examples. The techniques are not limited to being performed by one entity or multiple entities operating on one computing device.

[0051] At 802, the biometric authentication manager receives or captures one or more images, the one or more images representing the morphological features of the object from two or more perspectives, the one or more images being captured by one or more image sensors having two or more image capture perspectives. For example, the object may be a fingertip of a user, and the image sensor may be one or more cameras (e.g., a zoom camera, a macro camera, a telephoto camera, a slow-motion camera) incorporated into the back of the housing of the computing device. The camera may be a camera with two or more image capture perspectives. A camera with two or more image capture perspectives may be configured to perform the methods described herein. Alternatively, a camera with two or more image capture perspectives (e.g., a camera intended for general photography, a camera intended for telephoto) may be repurposed to perform the methods described herein. In an implementation, the biometric authentication manager may capture an image with one or more cameras with two or more image capture perspectives, or receive an image from it.

[0052] At 804, the biometric authentication manager determines topographical features of the object based on the one or more images. As an example, the topographical features may be details (eg, ridges, valleys, patterns) of a fingerprint of a user's fingertip.

[0053] At 806, the biometric authentication manager compares the topographical features of the object with previously captured topographical features of the previously imaged object to provide a comparison result. For example, the previously imaged object may be a fingertip of a user. Thus, the previously captured topographical features may be minutiae of a fingerprint. The comparison result may be based on correlations of general patterns (e.g., loops, spirals, arches), ridge features (e.g., ends, bifurcations, bridges), and other appropriate minutiae points between the current fingerprint image and the previously captured fingerprint image. For example, the current fingerprint image and the previously captured fingerprint image may be decomposed into a weighted sum of polar shapelet-base functions, where the fingerprint image is separated into components having rotational symmetry. The comparison result may also be based on correlations between the current fingerprint image and the previously captured fingerprint image of these components having rotational symmetry.

[0054] At 808, the biometric authentication manager determines that the object is the same object as the previously imaged object based on the comparison results from 806. The biometric authentication manager may do so when a threshold confidence level is reached, for example, the threshold confidence level may be a value representing the similarity between the previously imaged fingertip and the current fingertip (e.g., 90%, 95%). Alternatively, the biometric authentication manager may determine that the object is the same object as the previously imaged object within a margin of error. The margin of error may be a value representing the difference between the previously imaged fingertip and the current fingertip (e.g., 10%, 5%).

[0055] At 810, in response to determining that the object is the same object as the previously imaged object, the biometric authentication manager changes permissions to a resource associated with the computing device. The resource associated with the computing device may be an operating system of the computing device. In this example, changing permissions may be to unlock the computing device so the user can access and enjoy features of the operating system.

[0056] Additional Examples

[0057] In the following sections, additional examples are provided.

[0058] Example 1: A method, comprising: capturing one or more images, the one or more images representing morphological features of an object from two or more perspectives, the one or more images being captured by one or more image sensors having two or more image capturing perspectives; determining the morphological features of the object based on the one or more images; comparing the morphological features of the object with previously captured morphological features of a previously imaged object to provide a comparison result; determining that the object is the same object as the previously imaged object based on the comparison result and reaching a threshold confidence level; and changing permissions for resources associated with a computing device in response to determining that the object is the same object as the previously imaged object.

[0059] Example 2: A method as described in Example 1, wherein: the object having morphological features is a fingertip of a user; and the previously imaged object is the fingertip of the user.

[0060] Example 3: A method as in any of the previous examples, wherein the one or more image sensors are on a back side of the computing device.

[0061] Example 4: A method as described in Example 3, wherein the one or more image sensors are one or more cameras of the computing device, and the method further includes: before capturing the one or more images, displaying a prompt to place a finger on a camera lens cover, the camera lens cover covering the front of the one or more image sensors.

[0062] Example 5: A method as described in any of the previous examples, wherein the method is performed in response to the computing device being in an unlocked state but the resources of the computing device being in a locked state, and wherein changing the permissions to the resources unlocks the resources associated with the computing device.

[0063] Example 6: The method of Example 5, wherein the resource is a financial account or other high-powered resource requiring two-factor authentication, and the method provides one of the two factors of the two-factor authentication by determining that the object is the same object as the previously imaged object.

[0064] Example 7: The method of Example 6, wherein the other of the two factors of the two-factor authentication is a previous authentication for unlocking the computing device.

[0065] Example 8: The method of Example 6, wherein the other of the two factors of the two-factor authentication is a simultaneous fingerprint authentication performed on a side of the computing device that is different from the side on which the capturing of the one or more images is performed.

[0066] Example 9: A method as described in any of the previous examples, wherein the one or more images are received from an image capture device having a dual pixel image sensor configured to provide the two or more image capture angles of view.

[0067] Example 10: A method as described in any of the previous examples, wherein the one or more images are received from an image capture device having at least two image sensors configured to provide the two or more image capture perspectives, the at least two image sensors being physically separated from each other.

[0068] Example 11: A method as described in any of the previous examples, wherein determining the topographic features of the object generates an embedding, and the embedding is a numerical representation of the topographic features of the object.

[0069] Example 12: The method of Example 11, wherein comparing the topographic features compares the embedding to a previously generated embedding, the previously generated embedding being a numerical representation of the previously captured topographic features of the previously imaged object.

[0070] Example 13: The method as described in any of the previous examples also includes determining that the object having the morphological features is occluding the one or more image sensors before capturing the one or more images.

[0071] Example 14: The method of Example 13 further includes illuminating the object in response to determining that the object having the topographical feature is obstructing the one or more image sensors and before or simultaneously with capturing the one or more images.

[0072] Example 15: A method as described in Example 14, wherein illuminating the object is performed in a direction sufficient to change features from one of the two or more perspectives to a greater extent than another of the two or more perspectives, the change in the features achieving greater resolution of the morphological features of the object.

[0073] Example 16: A method as described in any of the previous examples, wherein the topographical features are details of fingerprints.

[0074] Example 17: A computing device comprising: one or more image sensors; one or more processors; and a memory storing instructions, which, when executed by the one or more processors, cause the one or more processors to implement a biometric authentication manager for providing biometric authentication using the one or more image sensors by performing the method of any of the preceding examples.

[0075] Example 18: A computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform a method as described in any one of Examples 1 to 16.

[0076] in conclusion

[0077] Throughout this discussion, examples are described in which a biometric authentication manager of a computing device (e.g., a smart phone) analyzes information associated with a user (e.g., fingerprint image data). In addition to the above description, a control may be provided to the user that allows the user to select whether and when the systems, programs, and / or features described herein may enable the collection of information (e.g., fingerprint image data). The biometric authentication manager may be configured to use the information only after receiving explicit permission from the user of the computing device to use the data. For example, where the biometric authentication manager may analyze image data for fingerprint features to authenticate a user, an individual user may be provided with an opportunity to provide input to control whether a program or feature of the computing device may collect and use the data. In addition, an individual user may have ongoing control over what programs may or may not process the information. In addition, the collected information may be preprocessed in one or more ways before it is transmitted, stored, or otherwise used so that personally identifiable information is removed. As described above, for example, the raw image data of a user's fingerprint may be converted (e.g., compressed) to an embedding (e.g., a string of numbers) that does not contain any personally identifiable information before it is stored on the CRM of the computing device. Thus, a user may have control over whether information about the user and the user's computing device is collected, and if so, how such information may be used by the biometric authentication manager, computing device, and / or remote computing system.

[0078] Unless the context dictates otherwise, the use of the word "or" herein may be viewed as use of an "inclusive or" or a term that permits inclusion or application of one or more items linked by the word "or" (e.g., the phrase "A or B" may be interpreted as permitting only "A," only "B," or both "A" and "B"). In addition, as used herein, a phrase referring to "at least one" of a list of items refers to any combination of those items, including single members. For example, "at least one of a, b, or c" may encompass a, b, c, ab, ac, bc, and abc, as well as any combination having multiples of the same element (e.g., aa, aaa, aab, aac, abb, acc, bb, bbb, bbc, cc, and ccc, or any other ordering of a, b, and c). In addition, the items represented in the drawings and the terms discussed herein may indicate one or more items or terms, and thus may refer interchangeably to the items and terms in the written description in either singular or plural form.

[0079] Although systems and techniques for repurposing one or more existing image sensors as biometric authentication scanners and implementations of devices that enable repurposing one or more existing image sensors as biometric authentication scanners have been described in language specific to certain features and / or methods, the subject matter of the appended claims is not necessarily limited to the specific features or methods described. Rather, the specific features and methods are disclosed as example implementations of repurposing one or more existing image sensors as fingerprint authentication scanners.

Claims

1. A method comprising: Capturing one or more images, wherein the one or more images represent topographic features of an object from two or more viewing angles, wherein the one or more images are captured by one or more image sensors having two or more image capturing viewing angles; determining the topographical features of the object based on the one or more images; comparing the topographical features of the object with previously captured topographical features of previously imaged objects to provide a comparison result; Based on the comparison result, determining that the object is the same object as the previously imaged object; as well as In response to determining that the object and the previously imaged object are the same object, permissions to a resource associated with a computing device are changed.

2. The method of claim 1, wherein: The object having the topographical feature is a fingertip of a user; and The previously imaged object is the fingertip of the user.

3. The method according to claim 1 or claim 2, wherein: The one or more image sensors are on a back side of the computing device, and the one or more image sensors are one or more cameras of the computing device, and the method further includes displaying a prompt to place a finger on a camera lens cover that covers a front side of the one or more image sensors before capturing the one or more images.

4. A method as claimed in any one of the preceding claims, wherein: The method is performed in response to the computing device being in an unlocked state but the resource of the computing device being in a locked state, and wherein changing the permissions to the resource unlocks the resource associated with the computing device.

5. The method of claim 4, wherein: The resource is a financial account or other high-powered resource requiring two-factor authentication, and the method provides one of the two factors of the two-factor authentication by determining that the object is the same object as the previously imaged object.

6. The method of claim 5, wherein: The other of the two factors of the two-factor authentication is a simultaneous fingerprint authentication performed on a side of the computing device that is different from the side on which the capturing of the one or more images is performed.

7. A method as claimed in any one of the preceding claims, wherein: The one or more images are received from an image capture device having a dual pixel image sensor configured to provide the two or more image capture angles of view.

8. A method as claimed in any one of the preceding claims, wherein: The one or more images are received from an image capture device having at least two image sensors configured to provide the two or more image capture angles of view, the at least two image sensors being physically separated from one another.

9. A method as claimed in any one of the preceding claims, wherein: Determining the topographical features of the object generates an embedding, the embedding being a numerical representation of the topographical features of the object.

10. The method of claim 9, wherein: Comparing the topographic features compares the embedding to a previously generated embedding that is a numerical representation of the previously captured topographic features of the previously imaged object.

11. The method of any of the preceding claims, further comprising determining, prior to capturing the one or more images, that the object having the topographical feature is occluding the one or more image sensors.

12. The method of claim 11, further comprising illuminating the object in response to determining that the object having topographical features is occluding the one or more image sensors and prior to or concurrently with capturing the one or more images.

13. The method of claim 12, wherein: Illuminating the object is performed in a direction sufficient to change features from one of the two or more perspectives by a greater amount than another of the two or more perspectives, the change in features enabling greater resolution of the topographical features of the object.

14. A computing device comprising: one or more image sensors; one or more processors; and A memory storing: Instructions which, when executed by the one or more processors, cause the one or more processors to implement a biometric authentication manager for providing biometric authentication using the one or more image sensors by performing the method as claimed in any one of the preceding claims.

15. A computer readable medium comprising instructions which, when executed by one or more processors, cause the one or more processors to perform the method of any one of claims 1 to 13.

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