Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Snur & Db Foundation

Chinese Academy of Sciences

University of Illinois

Northeastern University

University of Toronto

Jaejoon Han

Seungju Han

Chang Kyu Choi

Byungin Yoo

Changyong Son

Minsu Ko

Jungbae Kim

Jinwoo Son

Seon Min Rhee

Jihye Kim

Sungjoo Suh

Youngjun Kwak

Sungun Park

Wonjun Hwang

Deoksang Kim

Wonsuk Chang

Byung In Yoo

Kyuhong Kim

Wonjun Kim

Changkyo Lee

Insoo Kim

Dae-kyu Shin

Jingtao Xu

Heewon Kim

Jiwon Baek

Youngsung Kim

Seong-Jin Park

Jaehyoung Yoo

Hui Zhang

Changkyu Choi

Hao Feng

Hana Lee

Hyunjeong Lee

Sung-Jae Cho

Sangil Jung

Youngkyoo Hwang

Ju Hwan Song

Jiaqian Yu

Qiang Wang

Chao Zhang

Jiwhan Kim

Dongwook Lee

Namjoon Kim

Tianchu Guo

Xiabing Liu

Changin Choi

Yongchao Liu

Yiwei Chen

Jongseok Kim

Jiho Choi

Yong-Il Lee

Biao Wang

Seohyung Lee

Solae Lee

Changbeom Park

Bohyung Han

Jun Mo Kim

Hee Chul Jung

Sungheon Park

HyunJeong Lee

Yanhu Shan

Jaeyun Lee

Hangkai Tan

Jun Ho Yim

Liang Wang

Bing Yu

Joonah Park

Meng Ye

Yun Fu

Fei Duan

Kiho Cho

Sung Kwang Cho

Xuetao Feng

Yongjie Zhang

Hongwei Zhang

Lujin Gong

Yongzhen Huang

Haoxing Wang

Sung Ju Hwang

Dohwan Lee

Raymond Alexander Yeh

Alexander Gerhard Schwing

Seonmin Rhee

Zhengming Ding

Geuntae Bae

Byungln Yoo

Ming Shao

Ruslan Salakhutdinov

Colin Graber

Kikyung Kim

Provided is a method and apparatus with neural network operation. The method includes generating a first intermediate operation result by performing a first-order partial operation of a neural network layer on a first input line of a first area of a frame, generating a second intermediate operation result by performing another first-order partial operation of the neural network layer on a second input line of the first area, and generating an objective operation result of the neural network layer with respect to the first area based on a second-order partial operation performed on the first intermediate operation result and the second intermediate operation result.

Method and apparatus with neural network operation

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

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 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 liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Method and apparatus with liveness detection

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 target object detection method and apparatus are provided. The target object detection method and apparatus are applicable to fields such as artificial intelligence, object tracking, object detection, and image processing. An object is detected from a frame image of a video including a plurality of frame images based on a target template set including one or more target templates.

Video object detection and tracking method and apparatus

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

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 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

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

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Seoul, South Korea

Jaejoon Han