Samsung Electronics Co., Ltd.

Kyuhong Kim

Jaejoon Han

Sungun Park

Changkyu Choi

Sungjoo Suh

Namjoon Kim

Wonjun Kim

Chang Kyu Choi

Seon Min Rhee

Hana Lee

Sung-Jae Cho

Jiwhan Kim

Baehyung Kim

Youngsung Kim

Hyungsuk Kim

Sung-Un Park

Suhyun Park

Dohwan Lee

Yeunwook Lim

Sangmin Shin

Donghoon Kang

Insoo Kim

Jaeyun Lee

Jikwang Kang

Yeojeong Yoon

Yusun Cheong

Geuntae Bae

Youngihn Kho

Hyunsuk Choi

Jungbae Kim

Jongwu Baek

Seokwhan Chung

Jong Keun Song

Byungkyu Lee

Yun-Tae Kim

Changbyung Park

Kiho Cho

Jungho Kim

Juwan Park

Kyungil Cho

Kwangho Shin

Kwang Cheol Oh

Sungchan Park

Kyongho Lee

Jongkeun Song

A method with biometric information spoof detection includes extracting an embedding vector from an intermediate layer of a neural network configured to detect whether biometric information of a user is spoofed from an image including the biometric information; detecting first information regarding whether the biometric information is spoofed, based on the embedding vector; and detecting second information regarding whether the biometric information is spoofed based on whether the first information is detected, using an output vector output from an output layer of the neural network.

Method and apparatus with biometric information spoof detection

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.

Method and apparatus with anti-spoofing

A processor-implemented method with image preprocessing includes: transforming a fingerprint image into a frequency domain; suppressing a low-frequency region corresponding to a first noise component of the fingerprint image in the frequency domain; and restoring the frequency domain, in which the low-frequency region is suppressed, as an image.

Method and apparatus with image preprocessing

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.

Method and apparatus with input data classification

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.

Anti-spoofing method and apparatus

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.

Method and apparatus with biometric spoofing consideration

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.

Method and apparatus with fingerprint verification

A method and apparatus that detects whether biometric information is spoofed is provided. The method receives, from a sensor, first feature information including a static feature associated with biometric information of a user, and a dynamic feature obtained based on images related to the biometric information, detects whether the biometric information is spoofed based on a first score calculated based on the first feature information, fuses the first score with a second score calculated based on second feature information extracted from the images, based on a result of the detecting that the biometric information is spoofed based on the first score, and detects that the biometric information is spoofed based on a fused score.

Method and apparatus that detects spoofing of biometric information

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.

Method and apparatus for recognizing user based on on-device training

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.

User verification method and apparatus using generalized user model

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.

Speaker recognition method and apparatus

A method for updating biometric authentication data authenticates an input image using an enrollment database (DB) over a first length of time, the authentication including generating information for authenticating the input image, and updates the enrollment DB based on the first length time and the information for authenticating the input image.

Methods and apparatuses for updating user authentication data

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.

Apparatus with fake fingerprint detection and method with updating of model to prevent fake fingerprint

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Seoul, South Korea

Kyuhong Kim