Abstract:
An on-device training-based user recognition method includes performing on-device training on a feature extractor based on reference data corresponding to generalized users and user data, determining a registration feature vector based on an output from the feature extractor in response to the input of the user data, determining a test feature vector based on an output from the feature extractor in response to an input of test data, and performing user recognition on a test user based on a result of comparing the registration feature vector to the test feature vector.
Abstract:
A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.
Abstract:
A processor-implemented method includes: obtaining an enrollment fingerprint embedding vector corresponding to an enrollment fingerprint image; and generating a virtual enrollment fingerprint embedding vector, wherein the virtual enrollment fingerprint embedding vector has an environmental characteristic different from an environmental characteristic of the enrollment fingerprint image, and has a structural characteristic of the enrollment fingerprint image.
Abstract:
An electronic device and a method therefor are disclosed. The electronic device includes a memory for storing instructions which, when executed, cause a processor to display a user interface on a display, receive, from a touch sensor, first data indicating that an external object touches a part of the display, receive second data indicating the pressure applied against the display by the external object while the external object touches a part of the display, determine an application on the basis of a part of the first data, determine a change in the direction of the pressure applied against the display by the external object, on the basis of a part of the first data or the second data, wherein the direction is perpendicular to a first direction, and select a function associated with the application on the basis of a part of the change, and perform the selected function.
Abstract:
A beamforming apparatus includes: a signal output unit configured to output signals; a time difference corrector configured to correct a time difference between the signals; and a weight applier configured to apply a weight value to the signals, according to an error between the signals with the corrected time difference and a target delay pattern.
Abstract:
A transmit beamforming apparatus, receive beamforming apparatus, ultrasonic probe having the same, ultrasonic diagnostic apparatus, and beamforming method are provided. The transmit beamforming apparatus for transmitting ultrasound beams by using a plurality of ultrasonic transducer elements includes a transmit beamformer configured for forming a transmit signal pattern by applying a delay time to a transmit signal that corresponds to at least one of the plurality of ultrasonic transducer elements; and a transmission controller configured for determining a delay frequency to be applied in conjunction with the application of the delay time.
Abstract:
A speaker recognition method and apparatus receives a first voice signal of a speaker, generates a second voice signal by enhancing the first voice signal through speech enhancement, generates a multi-channel voice signal by associating the first voice signal with the second voice signal, and recognizes the speaker based on the multi-channel voice signal.
Abstract:
A method and apparatus with spoofing consideration is provided. The method includes implementing convolution block(s) of a machine learning model that determines whether biometric information in an input image is spoofed, including generating a feature map including channels for an input feature map for the input image using convolution layers of a convolution block of the convolution block(s), in response to a total number of input channels of the convolution block and a total number of output channels of the convolution block being different, matching the total number of input channels of the convolution block and the total number of output channels of the convolution block by adding a zero-padding channel to the input feature map using a skip connection structure, and generating output data for determining whether the biometric information is spoofed, dependent on the generated feature map and a result of the skip connection structure.
Abstract:
A user verification method and apparatus using a generalized user model is disclosed, where the user verification method includes generating a feature vector corresponding to a user based on input data corresponding to the user, determining a first parameter indicating a similarity between the feature vector and an enrolled feature vector enrolled for user verification, determining a second parameter indicating a similarity between the feature vector and a user model corresponding to generalized users, and verifying the user based on the first parameter and the second parameter.
Abstract:
A fingerprint verification method and a fingerprint verification apparatus performing the fingerprint verification method are disclosed. The fingerprint verification apparatus determines a first similarity between a query fingerprint image and each of registered fingerprint images, selects a target registered fingerprint image group from registered fingerprint image groups based on the first similarity, determines a second similarity between the query fingerprint image and each of registered fingerprint images in the target registered fingerprint image group based on matching relationship information between the registered fingerprint images in the target registered fingerprint image group, and determines whether fingerprint verification of the query fingerprint image is successful based on the second similarity.