Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Youngjun Kwak

Byung In Yoo

Jaejoon Han

Byungin Yoo

Youngsung Kim

Chang Kyu Choi

Yong-Il Lee

Jiho Choi

Hana Lee

Solae Lee

Sungheon Park

Seohyung Lee

Jungbae Kim

Seungju Han

Seon Min Rhee

Jingtao Xu

Jinwoo Son

Juwoan Yoo

Hyeongwook Yang

Changkyu Choi

Changkyo Lee

Hui Zhang

Chao Zhang

Jae Joon Han

Minsu Ko

Sungun Park

Wonjun Hwang

Hao Feng

Sangil Jung

Heewon Kim

Tushar Balasaheb Sandhan

Wonsuk Chang

Ju Hwan Song

Jiaqian Yu

Tianchu Guo

Yanhu Shan

Xiabing Liu

Yongchao Liu

Yunhao Zhang

Huxing Sun

Changyong Son

Changbeom Park

Taekyung Kim

Changick Kim

Byungln Yoo

Sungyoul Park

Byeongjun Park

Seoh Yung Lee

San Gil Jung

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

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

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 scene flow estimation method and apparatus are provided. The scene flow estimation method includes receiving a first feature pyramid and a second feature pyramid by encoding a first frame and a second frame of an input image through the same encoder, extracting a depth feature based on the first feature pyramid, extracting a motion feature based on the first feature pyramid and the second feature pyramid, generating an overall feature based on the depth feature and the motion feature, and estimating a scene flow based on the overall feature.

Method and apparatus with scene flow estimation

Disclosed is an object recognition method and object recognition apparatus. The object recognition method may include detecting an object region comprising an object in an input image, determining scores corresponding to regions of the object region, determining weights based on the scores and detection information of the object region, determining a final score based on the scores and the determined weights, and determining a result of recognizing the object based on the final score.

Object recognition method and object recognition apparatus

A device and method with object recognition is included. In one general aspect, an electronic device includes a camera sensor configured to capture a first image of a scene, the camera sensor is configured to perform at least one type of physical camera motion relative to the electronic device, the at least one type of physical camera motion includes rolling, panning, tilting, or zooming the camera sensor relative to the electronic device, and a processor configured to control the camera sensor to perform a physical motion of the physical camera motion type based on detecting an object in the first image, acquire a second image captured using the camera sensor as adjusted based on the performed physical motion, and recognize the object in the second image.

Device and method with object recognition

A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Method and apparatus for detecting liveness based on phase difference

An image processing method and apparatus using a neural network are provided. The image processing method includes generating a plurality of augmented features by augmenting an input feature, and generating a prediction result based on the plurality of augmented features.

Image processing method and apparatus using neural network

Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.

Method and apparatus with liveness verification

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 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 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

Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Method and apparatus for testing liveness

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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Seoul, South Korea

Youngjun Kwak