Abstract:
According to an embodiment, an input device of an electronic device, comprising a guide tube extending in a direction, a shaft mounted to be able to linearly move back and forth in the direction inside the guide tube, a first cam member rotatably mounted on the shaft inside the guide tube, being guided by the guide tube to linearly move along with the shaft in a first interval, and configured to rotate or linearly move about the shaft in a first position off the first interval to at least partially reenter into the first interval, a second cam member rotatably mounted on the shaft inside the guide tube, configured to linearly move in each of the first interval and a second interval adjacent to the first interval and differing from the first interval, and configured rotate and move about the shaft in the first position, a third cam member mounted on the shaft while facing the first cam member with the second cam member disposed the first cam member and the second cam member, the third cam member inside the guide tube and configured to linearly move back and forth along with the shaft, and a sensor module proximate to the first cam member, wherein as the third cam member linearly moves back and forth, the first cam member linearly moves and the second cam member linearly moves, and wherein the sensor module is configured to detect, at least, the first cam member reaching the first position to produce a first input signal.
Abstract:
A method and apparatus of fingerprint recognition are provided. A fingerprint recognition method involves obtaining an input fingerprint image in response to a fingerprint input from a user, determining pressure information relating to a pressure applied by the user to input the fingerprint image, and recognizing the user based on the obtained input fingerprint image and the pressure information.
Abstract:
A user authentication method using a fingerprint image, the user authentication method includes receiving at least a portion of a fingerprint image of a user; actuating a processor to divide the fingerprint image into a plurality of first sub-blocks; generate a set of input codes by encoding the first sub-blocks based on a coded model; measure a similarity between the set of the input codes and a set of registered codes included in a pre-registered binary codebook; and authenticate the user based on the similarity.
Abstract:
A method of preprocessing an image including biological information is disclosed, in which an image preprocessor may set an edge line in an input image including biological information, calculate an energy value corresponding to the edge line, and adaptively crop the input image based on the energy value.
Abstract:
A beamforming apparatus configured to beamform ultrasound waves transmitted through an ultrasound transducer having a two-dimensional transducer array includes a transmitter configured to output transmission pulses configured to drive elements constituting the transducer array, and a transmission switch configured to select at least two elements among the elements to form an aperture such that the transmission pulses drive the elements forming the aperture.
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:
An ultrasonic imaging apparatus includes: an ultrasound probe configured to transmit ultrasonic waves to a target region of an object in a plurality of directions, and to receive vibration waves generated from the object; and an image processor configured to generate image signals in the plurality of directions based on the vibration waves generated according to transmission of the ultrasonic waves in the plurality of directions, and to combine the image signals in the plurality of directions, wherein the ultrasound probe includes ultrasound elements configured to respectively generate ultrasonic waves of different frequencies, the ultrasonic waves intersecting each other in the target region of the object.
Abstract:
A processor-implemented anti-spoofing method includes: extracting an input embedding vector from input biometric information; obtaining a fake embedding vector of a predetermined fake model based on fake biometric information; obtaining either one or both of a real embedding vector of a predetermined real model and an enrolled embedding vector of an enrollment model the enrollment model being generated based on biometric information of an enrolled user; determining a confidence value of the input embedding vector based on the fake embedding vector and either one or both of the real embedding vector and the enrolled embedding vector; and determining whether the input biometric information is forged, based on the confidence value.
Abstract:
An anti-spoofing method includes detecting first information related to whether the biometric information is forged, based on a first output vector of a first neural network configured to detect whether the biometric information is forged from the input data, extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information, calculating a similarity value of the input embedding vector based on a fake embedding vector and either one or both of a real embedding vector and an enrollment embedding vector that are provided in advance, calculating a total forgery score based on the similarity value and a second output vector of the first neural network according to whether the first information is detected, and detecting second information related to whether the biometric information is forged, based on the total forgery score.
Abstract:
A processor-implemented method with input data classification includes: extracting an input embedding vector including a feature of biometric information of a user from input data including the biometric information; determining an adaptive embedding vector adaptive to the input embedding vector, based on a combination of a plurality of enrollment embedding vectors that are based on enrollment data; and classifying the input data based on a similarity between the input embedding vector and the adaptive embedding vector.