Samsung Electronics Co., Ltd.

Korea Advanced Institute of Science and Technology

Jiwon Baek

Seungju Han

Jaejoon Han

Insoo Kim

Seong-Jin Park

Minsu Ko

Hana Lee

Solae Lee

Hyejin Kang

Dongkyu Lim

Younghak Oh

Kyuok Choi

Wujong Kwon

Ju Hwan Song

Jookyung Woo

Wu-Jong Kwon

Sung Ju Hwang

Eunah Lee

Kikyung Kim

Hyejin Shim

An electronic device is provided. The electronic device includes a flexible display and at least one processor. The at least one processor can display a first execution screen of a first application in a first area of the flexible display, display a second execution screen of a second application in a second area sharing at least one edge with the first area, obtain a pre-defined event associated with the second application, in response to the pre-defined event, change the second execution screen displayed in the second area to a low power mode while maintaining the first execution screen in the first area, and while the second execution screen is in the low power mode, output some selected content in the second execution screen of the second application in consideration of at least one of a type or the properties of the second application.

Electronic device providing user interface

A liveness detection method and apparatus, and a facial verification method and apparatus are disclosed. The liveness detection method includes detecting a face region in an input image, measuring characteristic information of the face region, adjusting the measured characteristic information in response to the characteristic information not satisfying a condition, and performing a liveness detection on the face region with the adjusted characteristic information upon the measured characteristic information not satisfying the condition.

Method and apparatus with liveness detection

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 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

A processor-implemented neural network method includes: extracting, by a feature extractor of a neural network, a plurality of training feature vectors corresponding to a plurality of training class data of each of a plurality of classes including a first class and a second class; determining, by a feature sample generator of the neural network, an additional feature vector of the second class based on a mean vector and a variation vector of the plurality of training feature vectors of each of the first class and the second class; and training a class vector of the second class included in a classifier of the neural network based on the additional feature vector and the plurality of training feature vectors of the second class.

Method and apparatus with neural network meta-training and class vector training

A processor-implemented image processing method and apparatus are provided. The image processing method includes receiving an input image and rotation information associated with the input image, generating a feature vector of the input image based on pose information corresponding to the input image, generating an assistant feature vector which represents a target component according to a pose corresponding to the rotation information, based on the feature vector, the pose information, and the rotation information, and generating a target image which has the pose corresponding to the rotation information based on the feature vector and the assistant feature vector.

Image processing method and apparatus, and image processing training method

A processor-implemented facial image generating method includes: determining a first feature vector associated with a pose and a second feature vector associated with an identity by encoding an input image including a face; determining a flipped first feature vector by flipping the first feature vector with respect to an axis in a corresponding space; determining an assistant feature vector based on the flipped first feature vector and rotation information corresponding to the input image; determining a final feature vector based on the first feature vector and the assistant feature vector; and generating an output image including a rotated face by decoding the final feature vector and the second feature vector based on the rotation information.

Method and apparatus with facial image generating

A processor-implemented method with blur estimation includes: acquiring size information of an input image; resizing the input image to generate a target image of a preset size; estimating a blur of the target image; and estimating a blur of the input image based on the size information of the input image.

Method and apparatus with blur estimation

Display screen or portion thereof with graphical user interface

A processor-implemented neural network method includes: determining, using a neural network, a feature vector based on a training image of a first class among a plurality of classes; determining, using the neural network, plural feature angles between the feature vector and class vectors of other classes among the plurality of classes; determining a margin based on a class angle between a first class vector of the first class and a second class vector of a second class, among the class vectors, and a feature angle between the feature vector and the first class vector; determining a loss value using a loss function including an angle with the margin applied to the feature angle and the plural feature angles; and training the neural network by updating, based on the loss value, either one or both of one or more parameters of the neural network and one or more of the class vectors.

Growing community of inventors

Hwaseong-si, South Korea

Jiwon Baek