Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Chinese Academy of Sciences

University of Toronto

Byungin Yoo

Jaejoon Han

Chang Kyu Choi

Youngjun Kwak

Seungju Han

Jinwoo Son

Youngsung Kim

Jungbae Kim

Changyong Son

Jae-Joon Han

Wonsuk Chang

Jingtao Xu

Dae-kyu Shin

Deoksang Kim

Changkyu Choi

Changkyo Lee

Hui Zhang

Seon Min Rhee

Deheng Qian

Chao Zhang

Hao Feng

Tianchu Guo

Yanhu Shan

Sungjoo Suh

Wonjun Hwang

Wonjun Kim

Namjoon Kim

Yaozu An

Jun Mo Kim

Hee Chul Jung

Hyongeuk Lee

Jun Ho Yim

Jaeseok Choi

Liang Wang

Sujin Lee

Sangwon Lee

Yeun-bae Kim

Jun-Ho Park

Nahyup Kang

Hana Lee

Chang-kyu Choi

He Zheng

Jiho Choi

Seong-woon Kim

Biao Wang

Solae Lee

Yong-Il Lee

Dongkyung Nam

Namwoo Kim

Kinam Kwon

Sungheon Park

Hongseok Yang

Kwangil Hwang

Eunhee Kang

Yongzhen Huang

YangHo Cho

Xiabing Liu

Sungi Hong

Yongchao Liu

Ruslan Salakhutdinov

Yillmyung Yim

Sungkwang Cho

Changkyo Choi

An image restoration method includes determining degradation information indicating a degradation factor of a degraded image, tuning the degradation information based on a tuning condition, and generating a restored image corresponding to the degraded image by executing an image restoration network with the degraded image and the degradation information.

Image restoration method and apparatus

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

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.

An image restoration method includes determining an anchor image based on individual images of a burst image set, executing a feature extraction network based on the burst image set while using anchor information of the anchor image, and generating a restored image based on a feature map corresponding to an output of the feature extraction network.

Burst image-based image restoration method and apparatus

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

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.

Liveness test 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.

Disclosed is a method and apparatus for generating an image. The apparatus includes at least one processor and a memory. The processor is configured to obtain a wide image of an entire region, obtain one or more teleimages of one or more regions of interest (ROIs) of the entire region using a telecamera according to a capturing order of the telecamera determined for the one or more ROIs based on the wide image, match the wide image and the one or more teleimages, warp the one or more teleimages to the wide image based on a result of the matching, and stitch the one or more warped teleimages based on the wide image.

Method and apparatus for generating image

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 lightened neural network, method, and apparatus, and recognition method and apparatus implementing the same. A neural network includes a plurality of layers each comprising neurons and plural synapses connecting neurons included in neighboring layers. Synaptic weights with values greater than zero and less than a preset value of a variable a, which is greater than zero, may be at least partially set to zero. Synaptic weights with values greater than a preset value of a variable b, which is greater than zero, may be at least partially set to the preset value of the variable b.

Neural network method and apparatus

A method and an apparatus for recognizing an object are disclosed. The apparatus may extract a plurality of features from an input image using a single recognition model and recognize an object in the input image based on the extracted features. The single recognition model may include at least one compression layer configured to compress input information and at least one decompression layer configured to decompress the compressed information to determine the features.

Method and apparatus for recognizing object, and method and apparatus for training recognition model

Growing community of inventors

Seoul, South Korea

Byungin Yoo