Samsung Electronics Co., Ltd.

Skhynix Inc.

Korea Advanced Institute of Science and Technology

Northeastern University

Snur & Db Foundation

Imperial Innovations Limited

Other

Chinese Academy of Sciences

Yonsei University

Imperial College London

Chang Kyu Choi

Jaejoon Han

Seungju Han

Byung In Yoo

Byungin Yoo

Jae Joon Han

Jinwoo Son

Changyong Son

Minsu Ko

Du Sik Park

Sungjoo Suh

Sung Joo Suh

Youngjun Kwak

Jihye Kim

Youngsung Kim

Jae-Joon Han

Kwon Ju Yi

Deoksang Kim

Wonsuk Chang

Seung Ju Han

Jingtao Xu

Dae-kyu Shin

Wonjun Kim

Hyunjeong Lee

Seohyung Lee

Chang Kyo Lee

Hui Zhang

Kyuhong Kim

Sungun Park

Tianchu Guo

Hyong Euk Lee

Jungbae Kim

Sang Hyun Kim

Joon Ah Park

Namjoon Kim

Deheng Qian

Changkyo Lee

Du-Sik Park

Keun Woo Park

Hyun Surk Ryu

Hyungsuk Kim

Jung Bae Kim

Sangil Jung

Jiaqian Yu

Hee Seob Ryu

Qiang Wang

Bing Yu

Seon Min Rhee

Kee Chang Lee

Dae Han Kwon

Tae Kyun Kim

Wonjun Hwang

Hana Lee

Ji Yeun Kim

Yun Fu

Won Chul Bang

Biao Wang

Bohyung Han

Hee Chul Jung

Jun Mo Kim

Woon Bae Kim

Yiwei Chen

Nam Joon Kim

Xiabing Liu

Yongchao Liu

Jong Oh Kwon

Zhengming Ding

Danhang Tang

Alykhan Tejani

Hyung Jin Chang

Chao Zhang

Liang Wang

Do Kyoon Kim

Min Gu Kang

Fan Zhang

Dongwook Lee

Hae Kang Jung

Kyoobin Lee

Tae Jin Hwang

Hao Feng

Jung-Bae Kim

Ic Su Oh

He Zheng

Jun Yong Song

Xun Sun

Sun Ki Cho

Maolin Chen

Insoo Kim

Chilhee Chung

HyunJeong Lee

Young Kyoo Hwang

Yaozu An

Jaehyoung Yoo

Lujin Gong

Taek Sang Song

Dong Uc Ko

Yongzhen Huang

Zhilan Hu

Choon Sung Nam

Dohwan Lee

A neural network processing method and apparatus based on nested bit representation is provided. The processing method includes obtaining first weights for a first layer of a source model of a first layer of a neural network, determining a bit-width for the first layer of the neural network, obtaining second weights for the first layer of the neural network by extracting at least one bit corresponding to the determined bit-width from each of the first weights for the first layer of a source model corresponding to the first layer of the neural network, and processing input data of the first layer of the neural network by executing the first layer of the neural network based on the obtained second weights.

Neural network processing method and apparatus based on nested bit representation

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

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

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

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.

A method and apparatus for processing an image based on partial images. The method includes extracting a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN), updating a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region by inputting the extracted feature into the RNN, and generating an image processing result for the input image frame based on the updated hidden state.

Method and apparatus for processing image based on partial images

A face verifying method and apparatus. The face verifying method includes detecting a face region from an input image, generating a synthesized face image by combining image information of the face region and reference image information, based on a determined masking region, extracting one or more face features from the face region and the synthesized face image, performing a verification operation with respect to the one or more face features and predetermined registration information, and indicating whether verification of the input image is successful based on a result of the performed verification operation.

Face verifying method and apparatus

A processor-implemented liveness test method includes detecting a face region in a query image, the query image including a test object for a liveness test, determining a liveness test condition to be applied to the test object among at least one liveness test condition for at least one registered user registered in a registration database, determining at least one test region in the query image based on the detected face region and the determined liveness test condition, obtaining feature data of the test object from image data of the determined at least one test region using a neural network-based feature extractor, and determining a result of the liveness test based on the obtained feature data and registered feature data registered in the registration database and corresponding to the determined liveness test condition.

Liveness test method and apparatus

Disclosed is a processor-implemented data processing method in a neural network. A data processing apparatus includes at least one processor, and at least one memory configured to store instructions to be executed by the processor and a neural network, wherein the processor is configured to, based on the instructions, input an input activation map into a current layer included in the neural network, output an output activation map by performing a convolution operation between the input activation map and a weight quantized with a first representation bit number of the current layer, and output a quantized activation map by quantizing the output activation map with a second representation bit number based on an activation quantization parameter.

Method and apparatus with neural network parameter quantization

A convolutional neural network (CNN) processing method includes selecting a survival network in a precision convolutional network based on a result of performing a high speed convolution operation between an input and a kernel using a high speed convolutional network, and performing a precision convolution operation between the input and the kernel using the survival network.

Convolutional neural network (CNN) processing method and apparatus performing high speed and precision convolution operations

Disclosed is a target tracking method and apparatus. The target tracking apparatus includes a processor configured to obtain a first depth feature from a target region image and obtain a second depth feature from a search region image, obtain a global response diagram between the first depth feature and the second depth feature, acquire temporary bounding box information based on the global response diagram, updated the second depth feature based on the temporary bounding box information, obtain local feature blocks based on the first depth feature, obtain a local response diagram based on the local feature blocks and the updated second depth feature, and determine output bounding box information based on the local response diagram.

Method and apparatus for tracking target

A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.

Method and apparatus for acquiring feature data from low-bit image

A three-dimensional (3D) image-based facial verification method and apparatus is provided. The facial verification method may include capturing a facial image of a 3D face of a user, determining an occluded region in the captured facial image by comparing the captured facial image and an average facial image, generating a synthetic image by synthesizing the captured facial image and the average facial image based on the occluded region, and verifying the user based on the synthetic image.

Facial verification method and apparatus based on three-dimensional (3D) image

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 liveness test method and apparatus is disclosed. A processor implemented liveness test method includes extracting an interest region of an object from a portion of the object in an input image, performing a liveness test on the object using a neural network model-based liveness test model, the liveness test model using image information of the interest region as provided first input image information to the liveness test model and determining liveness based at least on extracted texture information from the information of the interest region by the liveness test model, and indicating a result of the liveness test.

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Seongnam-si, South Korea

Chang Kyu Choi