A method of recognizing individuals by means of at least one processor executing a recognition algorithm comprising the steps of: detecting biometric characteristics of a finger of a candidate for recognition while writing a signature by pressing the finger against a signature surface; encoding both the written signature and also the biometric characteristics in order to form signature data and biometric data of the candidate; and using the algorithm to compare the signature data and the biometric data of the candidate with signature data and fingerprint biometric data belonging to at least one individual and stored on a data medium.

A non-transitory computer-readable medium encoded with a computer-readable program which, when executed by a first processor, will cause a computer to execute a method of authenticating a user, the method including training a plurality of models. The method additionally includes sending, by a computer, an acoustic signal. Further, the method includes receiving, by the computer, a reflected acoustic signal, wherein the reflected acoustic signal comprises information about a signature. Moreover, the method includes receiving, by the computer, motion data about the information about the signature. The method additionally includes comparing the information about the signature to a model to generate a score. Additionally, the method includes deciding, based on the score, if the information about the signature is within a predetermined tolerance level.

Methods, systems, and computer-accessible mediums are described to authenticate a user using a user's handwriting style rather than using the user's signature through adaptive handwriting challenges which are verified using the machine learning technique of a generative adversarial network. An exemplary system, method, and computer-accessible medium can include, for example, receiving at a first entity, a request for authentication of a user associated with a user identifier; sending from the first entity to the user, a first adaptive phrase; receiving at the first entity, a digital representation of a human transcription of the first adaptive phrase; performing an artificial-intelligence based comparison between the received digital representation of the human transcription of the first adaptive phrase and a user-specific computer-generated discriminator or user-specific computer-generated model of the first adaptive phrase; generating a first score based on the comparison; sending from the first entity (i) an authentication message if the first score is equal to or above a pre-determined threshold or (ii) a failure message if the first score is below a pre-determined threshold.

Techniques for detecting counterfeit checks include using sensors to determine correspondence between a portion of sensed check information and related information; analyzing behavior related to accounts to a check deposit transaction; and controlling check depositing through user interface controls or communication. These techniques can be employed individually or in combination.

Embodiments of the invention are generally directed to systems, methods, devices, and machine-readable mediums for implementing gesture-based signature authentication. In one embodiment, a method may involve recording a first gesture-based signature and storing the recorded first gesture-based signature. Then the method compares the first gesture-based signature with a second gesture-based signature. Then the method verifies the first gesture-based signature as authentic when the first gesture-based signature is substantially similar to the second gesture-based signature.

Embodiments of the invention are generally directed to systems, methods, devices, and machine-readable mediums for implementing gesture-based signature authentication. In one embodiment, a method may involve recording a first gesture-based signature and storing the recorded first gesture-based signature. Then the method compares the first gesture-based signature with a second gesture-based signature. Then the method verifies the first gesture-based signature as authentic when the first gesture-based signature is substantially similar to the second gesture-based signature.

Disclosed herein is a signature verification apparatus including a first verification circuitry verifying a user's signature by comparing dynamic signature data indicating a change of a user's writing state over time during signing of his or her name by the user and reference data for dynamic signature, a second verification circuitry verifying the user's signature by comparing static signature data indicating a writing path during signing of his or her name by the user and reference data for static signature, and a data registration circuitry registering the reference data for dynamic signature and register the reference data for static signature. In a case where the reference data for static signature has yet to be registered by the data registration circuitry, the second verification circuitry verifies the user's signature by regarding static signature data generated from the reference data for dynamic signature already registered as the reference data for static signature.

Handwriting verification methods and related computer systems, and handwriting-based user authentication methods and related computer systems are disclosed. A handwriting verification method comprises obtaining a handwriting test sample containing a plurality of available parameters, extracting geometric parameters, deriving geometric features comprising an x-position value and a y-position value for each of a plurality of feature points in the test sample, performing feature matching between geometric features of the test sample and a reference sample, determining a handwriting verification result based at least in part on the feature matching, and outputting the handwriting verification result. Techniques and tools for generating and preserving electronic handwriting data also are disclosed. Raw handwriting data is converted to a streamed format that preserves the original content of the raw handwriting data. Techniques and tools for inserting electronic handwriting data into a digital image also are disclosed.

The present invention provides a computer implemented method, a system, and a computer program product for verifying a writing of a user. In an exemplary embodiment, the present invention includes in response to receiving a writing on a pressure sensing touchpad logically coupled a computer system, recording a position and a pressure of one or more points of the writing via a pressure sensing touchscreen, executing a set of logical operations normalizing the writing, comparing the normalized writing to one or more stored writing parameters, executing a set of logical operations determining the normalized writing is within a tolerance of writing parameter deviation limits, thereby verifying the writing, and in response to determining the writing is within the tolerance of writing parameter deviation limits, storing, by the computer system, a value indicating that the writing is valid.

The application discloses a method for remote interview signature, an electronic device and a computer-readable medium, comprising steps of: step 01, the client end inquiring whether there is an interview signature task, if so, then going to step 02, if not, exits; Step 02, the user terminal sending the interview signature request to the agent end, the interview signature request including the user information and the service information, the agent end confirming the user information and the service information; Step 03, the agent end authenticating the user, if the authentication passes, then going to step 04, and if the authentication fails, the user is reminded to go to the counter to handle the interview signature; Step 04, the agent end sending the face tag file to the user terminal, the signature file being named and archived at the agent end. user terminal.