Samsung Electronics Co., Ltd.

Northeastern University

Sungjoo Suh

Wonjun Kim

Chang Kyu Choi

Jaejoon Han

Hyunjeong Lee

Seungju Han

Youngsung Kim

Wonjun Hwang

Kyuhong Kim

Hyungsuk Kim

Jae-Joon Han

Namjoon Kim

Changkyu Choi

Jihye Kim

Byungin Yoo

Minsu Ko

Jungbae Kim

Jiwhan Kim

Yun Fu

Huijin Lee

Zhengming Ding

Changyong Son

Joonah Park

Hao Feng

Biao Wang

Xuetao Feng

Hongwei Zhang

Lujin Gong

Do-Ha Hwang

Young Chun Ahn

Wissam Baddar

Eunju Cha

Ming Shao

Ho-Kuen Shin

Training of a machine learning model is included. An electronic device includes a memory storing a machine learning model including a discriminator model, and a processor configured to extract, from a training image, a first image patch and a second image patch at least partially overlapping the first image patch, extract, from the first image patch, a first feature map based on a layer of the discriminator model, extract, from the second image patch, a second feature map based on the layer, extract a first partial feature map from a projected map that is projected based on the first feature map, and train the discriminator model based on a first objective function value generated based on a second partial feature map and the first partial feature map, wherein the second partial feature map corresponds to a part of the extracted second feature map.

Electronic device and method with machine learning training

A processor-implemented method includes: generating a first sample image and a second sample image by performing data augmentation on an input training image; generating a first feature map of the first sample image and a second feature map of the second sample image by performing feature extraction on the first sample image and the second sample image using an encoding model; determining first loss data according to a relationship between first feature vectors of the first feature map and second feature vectors of the second feature map; estimating relative geometric information of the first feature map and the second feature map using a relationship estimation model; determining second loss data according to the relative geometric information, based on label data according to a geometric arrangement of the first sample image and the second sample image in the input training image; and training the encoding model and the relationship estimation model, based on the first loss data and the second loss data.

Method and apparatus with recognition model training

An image processing method includes extracting a first region in a first image by inputting the first image to a pretrained neural network, upscaling a resolution of the first region by performing neural network-based super resolution processing on the first region, and upscaling a resolution of a second region in the first image from which the first region is excluded by performing interpolation on the second region.

Image processing apparatus and method

A user verification apparatus may perform user verification using multiple biometric verifiers. The user verification apparatus may set a termination stage of one or more biometric verifiers. Multiple biometric verifiers may be used to generate outputs, for which separate termination stages are set to establish a particular combination of set termination stages associated with the multiple biometric verifiers, and the user verification apparatus may fuse outputs of the biometric verifiers based on the particular combination of set termination stages. The user verification apparatus may verify a user based on a result of the fusing, and an unlocking command signal may be generated based on the verifying. The unlocking command signal may be generated to selectively grant access, to the verified user, to one or more elements of a device. The device may be a vehicle.

Method and apparatus for verifying user using multiple biometric verifiers

A fingerprint verification method includes selecting one or more first fingerprint groups from among a plurality of fingerprint groups based on an input fingerprint image, each fingerprint group of the plurality of fingerprint groups including partial fingerprint images; and determining whether verification is successful based on the input fingerprint image and each of the partial fingerprint images included in the one or more first fingerprint groups.

Fingerprint verification 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 method and apparatus with fingerprint verification are disclosed. A processor implemented method includes obtaining a query fingerprint image through a fingerprint sensor, obtaining a processed fingerprint image by performing image processing on the obtained query fingerprint image, obtaining a blended fingerprint image by blending the query fingerprint image and the processed fingerprint image, and performing fingerprint verification based on the blended fingerprint image and a registered fingerprint image.

Method and apparatus with fingerprint verification

A method with generation of a transformed image includes: receiving an input image; extracting, from the input image, coefficients corresponding to semantic elements of the input image; selecting at least one first target coefficient, among the coefficients, corresponding to at least one target semantic element that is to be changed among the semantic elements of the input image; changing the at least one first target coefficient; and generating a transformed image from the input image by applying the coefficients, including the changed at least one first target coefficient, to basis vectors used to represent the semantic elements of the input image in an embedding space of a neural network, the basis vectors corresponding to the semantic elements of the input image.

Method and apparatus with generation of transformed image

An apparatus and method for detecting a fake fingerprint is disclosed. The apparatus may divide an input fingerprint image into blocks, determine an image quality assessment (IQA) value associated with each block, determine a confidence value based on the IQA values using a confidence determination model, and determine whether an input fingerprint in the input fingerprint image is a fake fingerprint based on the determined confidence value.

Method and apparatus for detecting fake fingerprint, and method and apparatus for recognizing fingerprint

A user recognition method and apparatus, the user recognition method including performing a liveness test by extracting a first feature of a first image acquired by capturing a user, and recognizing the user by extracting a second feature of the first image based on a result of the liveness test, is provided.

Liveness testing methods and apparatuses and image processing methods and apparatuses

A method of recognizing a feature of an image may include receiving an input image including an object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognizing an element corresponding to the object based on the first feature information and the second feature information.

Image recognition method and apparatus, image verification method and apparatus, learning method and apparatus to recognize image, and learning method and apparatus to verify image

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.

Method of preprocessing image including biological information

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Seoul, South Korea

Sungjoo Suh