Samsung Electronics Co., Ltd.

Snur & Db Foundation

Korea Advanced Institute of Science and Technology

Seoul National University

Seungju Han

Jaejoon Han

Chang Kyu Choi

Minsu Ko

Deoksang Kim

Byungin Yoo

Changyong Son

Seon Min Rhee

Jihye Kim

Jinwoo Son

Jiwon Baek

Sungjoo Suh

Jae-Joon Han

Jungbae Kim

Dae-kyu Shin

Sungun Park

Youngjun Kwak

Hana Lee

Jongseok Kim

Byung In Yoo

Hyochul Kim

Youngsung Kim

Suyeon Lee

Minjung Kim

Changkyu Choi

Wonsuk Chang

Insoo Kim

Seong-Jin Park

Heewon Kim

Solae Lee

Ju Hwan Song

Wonjun Hwang

Sungdo Choi

Unjeong Kim

Byung Kwan Kim

Jiho Choi

Jong-Sok Kim

Yong-Il Lee

Bohyung Han

Joonah Park

Wonjun Kim

Hyunjeong Lee

Jung-Bae Kim

Hyuck Choo

Chilhee Chung

Gunhee Kim

Hui Li

Sung Ju Hwang

Hyun Oh Song

Gaon An

Seonmin Rhee

Seon-Min Rhee

Zidong Guo

Kikyung Kim

Dongyeon Woo

Jaekyeom Kim

Youngdong Kim

Jongin Lim

Byungjai Kim

Kim Jungbae

Min Rhee Seon

Yungin Yoo

Ko Minsu

A method with image processing includes: generating a first surface normal image comprising surface normal vectors corresponding to pixels of a first depth image; and applying the first depth image and the first surface normal image to a first neural network, and acquiring a second depth image by changing the first depth image using the first neural network. The first neural network generates the second depth image to have an improved quality compared to the first depth image, based on an embedding vector that comprises a feature of the first depth image and a feature of the first surface normal image.

Method and apparatus with image processing for processing depth images

A method and apparatus with landmark coordinate prediction are provided. The method includes generating a multi-stage feature map for landmarks of a face image through a staged convolutional network, generating an initial query matrix by fully connecting a last-stage feature map in the multi-stage feature map using a fully connected network, where a total number of feature elements in the initial query matrix is equal to a total number of predicted landmarks of the face image, generating a memory feature matrix by flattening and connecting the multi-stage feature map, generating the predicted landmark coordinates by inputting the memory feature matrix and the initial query matrix to a decoder network of plural cascaded decoder networks.

Method and apparatus with face landmark coordinate prediction

Disclosed is an image fusion method and apparatus. The fusion method includes detecting first feature points of an object in a first image frame from the first image frame; transforming the first image frame based on the detected first feature points and predefined reference points to generate a transformed first image frame; detecting second feature points of the object in a second image frame from the second image frame; transforming the second image frame based on the detected second feature points and the predefined reference points to generate a transformed second image frame; and generating a combined image by combining the transformed first image frame and the transformed second image frame.

Method and apparatus with image fusion

A facial verification method and apparatus is disclosed. The facial verification method includes detecting a face region in an input image, determining whether the detected face region represents a partial face, in response to a determination that the detected face region represents the partial face, generating a synthesized image by combining image information of the detected face region and reference image information, performing a verification operation with respect to the synthesized image and predetermined first registration information, and indicating whether facial verification of the input image is successful based on a result of the performed verification operation.

Facial verification method and apparatus

An authentication method and apparatus using a transformation model are disclosed. The authentication method includes generating, at a first apparatus, a first enrolled feature based on a first feature extractor, obtaining a second enrolled feature to which the first enrolled feature is transformed, determining an input feature by extracting a feature from input data with a second feature extractor different from the first feature extractor, and performing an authentication based on the second enrolled feature and the input feature.

Authentication method and apparatus with transformation model

A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Method and apparatus with liveness test and/or biometric authentication

A liveness detection method and apparatus, and a facial verification method and apparatus are disclosed. The liveness detection method includes detecting a face region in an input image, measuring characteristic information of the face region, adjusting the measured characteristic information in response to the characteristic information not satisfying a condition, and performing a liveness detection on the face region with the adjusted characteristic information upon the measured characteristic information not satisfying the condition.

Method and apparatus with liveness detection

An image recognition method includes: receiving an input image of a first quality; extracting an input feature of a second quality of the input image from the input image by inputting the input image to an encoding model in an image recognizing model; and generating a recognition result for the input image based on the input feature.

Method and apparatus with image recognition

Disclosed is a liveness test method and liveness test apparatus. The liveness test method includes determining a presence of a subject using a radar sensor, performing a first liveness test on the subject based on radar data obtained by the radar sensor, in response to the subject being present, acquiring image data of the subject using an image sensor, in response to a result of the first liveness test satisfying a first condition, and performing a second liveness test on the subject based on the image data.

Liveness test method and liveness test apparatus

An image processing method includes receiving an input image and a guide image corresponding to the input image, extracting informative features from the input image and the guide image to enhance the input image, selectively obtaining a first feature for the input image from among the informative features, and processing the input image based on the first feature.

Image processing method and image processing apparatus

A processor-implemented radar data recognition method including generating a plurality of pieces of input data, with respectively different dimension configurations, based on radar data of an object; and outputting a recognition result of the object based on the generated plurality of pieces of input data using a recognition model.

Method and apparatus with radar data recognition

A method and apparatus for generating a depth image are provided. The apparatus receives an input image, extracts a feature corresponding to the input image, generates features for each depth resolution by decoding the feature using decoders corresponding to different depth resolutions, estimates probability distributions for each depth resolution by progressively refining the features for each depth resolution, and generates a target depth image corresponding to the input image based on a final estimated probability distribution from among the probability distributions for each depth resolution.

Method and apparatus for generating depth image

Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.

Face verification method and apparatus

A method with access authority management includes: receiving an input image comprising a region of at least one portion of a body of a user; determining whether the user corresponds to multiple users or a single user using the region of the at least one portion of the body; performing a verification for the user based on a face region in the input image, in response to the determination that the user is the single user; determining whether the input image is a real image or a spoofed image based on whether the verification is successful; and allowing an access authority to a system to the user, in response to the determination that the input image is the real image.

Method and apparatus with access authority management

Disclosed is a face verification method and apparatus. A mobile device may include one or more processors configured to obtain one or more images for a user, ascertain whether any of the one or more images correspond to respective user distances, from the user to the mobile device, outside of a threshold range of distances, and selectively, based on a result of the ascertaining, perform verification using a first verification threshold for any of the one or more images ascertained to correspond to the respective user distances that are outside the threshold range of distances, and perform verification using a less strict second verification threshold for any of the one or more images that have been ascertained to not correspond to the respective user distances that are outside the threshold range of distances.

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Seoul, South Korea

Seungju Han