Described are various embodiments of a digital user authentication device, the device comprising: a user authentication interface operable to receive as input unique user identification data required to execute a digital user authentication process; a distinct physiological sensor operable to interface with the user to acquire a physiological signal from the user to automatically confirm a live user presence during said authentication process; and a digital data processor and computer-readable memory operable to execute computer-readable instructions to invoke said user authentication process based on said unique user identification data while confirming said live user presence based on said physiological signal such that a successful user authentication is only concluded upon confirmation of said live user presence during said authentication process. Various authentication, access authorization and revocation systems and processes are also described.

A fingerprinting solution that uses neural network (NN) based trained Machine Learning (ML) modules in combination with traditional image processing for contactless fingerprint capture, liveness detection to rule out fake fingers, and fingerprint matching using a portable handheld device with integrated camera, thereby eliminating the need for a special device dedicated for fingerprinting. The trained NN modules detect the size and direction of fingers in the captured image, check if fingers are reversed in the image (thereby making nails visible), check if the thumb of the correct hand is captured, and generate fixed-length fingerprint templates for subsequent matching of fingerprints. Three dimensional (3D) depth map of a finger is used to bring the fingerprint resolution to 500 dpi and eliminate distortion caused by the curvature of the finger shape to improve accuracy while scaling and flattening a fingerprint image. The solution facilitates contactless-to-contactless as well as contactless-to-contact based fingerprint matching.

A method for detection of altered fingerprints includes receiving, by at least one processor, an image of a fingerprint from a fingerprint reader. The image has an image resolution. The processor determines a spatial location of the fingerprint within the image. The processor crops the image around the spatial location to provide a cropped image. The processor generates multiple derived images using the cropped image, such that each derived image has the image resolution. The processor generates a multiple-channel image using the derived images. The processor scales the multiple-channel image to an image size. The processor generates a score using a machine learning model. The score is based on the multiple-channel image and is indicative of a likelihood that the fingerprint has been altered.

An automated, dynamic digital financial management tool is described herein. The financial management tool enables a user to access all forms of payments, debt and transactions to be deducted automatically depending on which form of payment is chosen. The financial management tool documents every item/service purchased. The documentation enables a company/manufacturer/store to provide marketing and recall information to the user. The financial management tool is able to include any and/or all financial aspects of a user's life. The financial management tool is able to be implemented using a universal card. The financial management tool is able to include a pay station to receive payments. The financial management tool is able to include automated services. An automated, dynamic health management system is also described herein. The financial management tool enables automatic stock purchases based on product purchases.

The present teaching relates to method, system, medium, and implementations for detecting liveness. When an image is received with visual information claimed to represent a palm of a person, a region of interests (ROI) in the image that corresponds to the palm is identified. Each of a plurality of fake palm detectors individually generates an individual decision on whether the ROI corresponds to a specific type of fake palm that the fake palm detector is to detect. Such individual decisions from the plurality of fake palm detectors are combined to derive a liveness detection decision with respect to the ROI.

An image acquisition device includes a single sensor having organic photodetectors, an angular filter, and a color filter. The color filter has one or a plurality of first portions adapted to give way to at least one wavelength in the visible range, and one or a plurality of second portions filtering wavelengths outside of red and/or near infrared. The second portion has a surface area approximately equal to a surface area of one of the photodetectors or a surface area greater than or equal to the surface area of four of the photodetectors.

Technologies are presented herein in support of a system and method for performing fingerprint recognition. Embodiments of the present invention concern a system and method for capturing a user's biometric features and generating an identifier characterizing the user's biometric features using a mobile device such as a smartphone. The biometric identifier is generated using imagery captured of a plurality of fingers of a user for the purposes of authenticating/identifying the user according to the captured biometrics and determining the user's liveness. The present disclosure also describes additional techniques for preventing erroneous authentication caused by spoofing. In some examples, the anti-spoofing techniques may include capturing one or more images of a user's fingers and analyzing the captured images for indications of liveness.

A display device includes a display panel and a fingerprint authentication device disposed under the display panel. The fingerprint authentication device includes a sensor device and an optical filter. The sensor device includes a first area and a second area in a plan view and sensing light passing through the display panel. The optical filter is disposed between the display panel and the sensor device, overlaps the first area and does not overlap the second area in the plan view, and blocks the light of a certain wavelength range.

A processor-implemented method includes: obtaining an input embedding vector corresponding to an input fingerprint image for authentication; determining a confidence value of the input embedding vector based on fingerprint data of an initial model including either one or both of a trained real fingerprint determination model and a trained fake fingerprint determination model that are provided in advance; and updating the initial model based on the input embedding vector, in response to the confidence value being greater than or equal to a first threshold.

In a method for multipath reflection correction of acoustic signals received at an ultrasonic sensor, characteristics of multipath reflection signals of the ultrasonic sensor are accessed, wherein the characteristics of the multipath reflection signals include a relationship of primary signal contributions to multipath reflection signal contributions for acoustic signals received at the ultrasonic sensor at a plurality of times of flight for a plurality of locations of the ultrasonic sensor. Acoustic signals are received at the ultrasonic sensor over a time of flight range while a target is interacting with the ultrasonic sensor, wherein the acoustic signals include a primary signal contribution and a multipath reflection signal contribution. The characteristics of the multipath reflection signals are compared to received acoustic signals. The primary signal contribution of the received acoustic signals is determined at a plurality of times of flight of the time of flight range based on the characteristics of the multipath reflection signals.