Samsung Electronics Co., Ltd.

Lg Electronics Inc.

Lg Display Co., Ltd.

Yonsei University

Youngsung Kim

Youngjun Kwak

Chang Kyu Choi

Byungin Yoo

Sungjoo Suh

Byung In Yoo

Wonjun Kim

Jaejoon Han

Jingtao Xu

Hui Zhang

Kyuhong Kim

Hyungsuk Kim

Jae-Joon Han

Hyunjeong Lee

Deheng Qian

Yong-Il Lee

Brian Chung

Tianchu Guo

Chao Zhang

Seungju Han

Seon Min Rhee

Minsu Ko

Hao Feng

Seohyung Lee

Jiho Choi

Heewon Kim

Wonsuk Chang

Ju Hwan Song

Sumin Yoon

Yanhu Shan

Sooyeon Kim

Sungtae Lee

Jaeho Kim

Sangdon Park

Dong-Hyuk Lee

Changkyu Choi

Mu-sik Kwon

Hana Lee

Jinha Heo

Jongjin Woo

Sungtae Kim

He Zheng

Minsoo Park

Sangil Jung

Bumgi Min

Seunggyu Lee

Hyeongseok Kim

Sangyoun Lee

Hyobin Lee

Byungln Yoo

Keunbok Song

Seokho Kwon

Beom-seok Oh

Jaeho Hwang

Youngbong Cho

Jaeheung Kim

Dukho Cho

Sukho Son

Kar-Ann Toh

Xiabing Liu

A compressor control apparatus and method differently compensate for a duty ratio of a control signal during a period in which the compressor performs a compression stroke and a period in which the compressor performs a suction stroke, respectively, to generate the control signal for controlling a compressor.

Apparatus for controlling compressor, compressor and method for controlling compressor

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 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

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.

Liveness test method and apparatus and biometric authentication method and apparatus

A liveness test method and apparatus is disclosed. The liveness test method includes detecting a face region in an input image for a test target, implementing a first liveness test to determine a first liveness value based on a first image corresponding to the detected face region, implementing a second liveness test to determine a second liveness value based on a second image corresponding to a partial face region of the detected face region, implementing a third liveness test to determine a third liveness value based on an entirety of the input image or a full region of the input image that includes the detected face region and a region beyond the detected face region, and determining a result of the liveness test based on the first liveness value, the second liveness value, and the third liveness value.

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 neural network recognition method includes obtaining a first neural network that includes layers and a second neural network that includes a layer connected to the first neural network, actuating a processor to compute a first feature map from input data based on a layer of the first neural network, compute a second feature map from the input data based on the layer connected to the first neural network in the second neural network, and generate a recognition result based on the first neural network from an intermediate feature map computed by applying an element-wise operation to the first feature map and the second feature map.

Neural network based recognition apparatus and method of training neural network

A processor implemented method of processing a facial expression image, the method includes controlling a camera to capture a first facial expression image and a second facial expression image, acquiring a first expression feature of the first facial expression image, acquiring a second expression feature of the second facial expression image, generating a new expression feature dependent on differences between the acquired first expression feature and the acquired second expression feature, and adjusting a target facial expression image based on the new expression feature.

Facial expression image processing method and apparatus

A processor-implemented method including implementing a deep neural network (DNN) model using input data, generating, by implementing the DNN model, first output data from the DNN model, changing the DNN model, generating, by implementing the changed DNN model using the input data, second output data of the changed DNN model, and determining result data by combining the first output data and the second output data.

Method and apparatus with deep neural network model fusing

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 method and apparatus with emotion recognition acquires a plurality of pieces of data corresponding a plurality of inputs for each modality and corresponding to a plurality of modalities; determines a dynamics representation vector corresponding to each of the plurality of modalities based on a plurality of features for each modality extracted from the plurality of pieces of data; determines a fused representation vector based on the plurality of dynamics representation vectors corresponding to the plurality of modalities; and recognizes an emotion of a user based on the fused representation vector.

Method and apparatus with emotion recognition

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

Growing community of inventors

Suwon-si, South Korea

Youngsung Kim