Samsung Electronics Co., Ltd.

Ju Hwan Song

Byung In Yoo

Seon Min Rhee

Heewon Kim

Jaejoon Han

Jihye Kim

Seungju Han

Changbeom Park

Youngjun Kwak

Minsu Ko

Youngsung Kim

Jiho Choi

Yong-Il Lee

HyunJeong Lee

Changyong Son

Dongwook Lee

Jiwon Baek

Seungwook Kim

Insoo Kim

Seong-Jin Park

Hong Youl Ryu

Hwa Young Woo

Jeong Mo Kim

Vladislav Ri

A processor-implemented method with target object tracking includes: setting a search area for a target object included in an input image based on a position of a first target box in a template image; selecting a network path from a plurality of network paths of a neural network model according to a resizing ratio of a size of another image that is input to the neural network model to a size of the search area; and tracking the target object by estimating a position of a second target box corresponding to the target object in the input image according to the selected network path.

Method and apparatus with target object tracking

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 user authentication method and a user authentication apparatus acquire an input image including a frontalized face of a user, calculate a confidence map including confidence values, for authenticating the user, corresponding to pixels with values maintained in a depth image of the frontalized face of the user among pixels included in the input image, extract a second feature vector from a second image generated based on the input image and the confidence map, acquire a first feature vector corresponding to an enrolled image, and perform authentication of the user based on a correlation between the first feature vector and the second feature vector.

User authentication apparatus, user authentication method and training method for user authentication

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 processor-implemented method with object tracking includes: determining an initial template image based on an input bounding box and an input image; generating an initial feature map by extracting features from the initial template image; generating a transformed feature map by performing feature transformation adapted to objectness on the initial feature map; generating an objectness probability map and a bounding box map indicating bounding box information corresponding to each coordinate of the objectness probability map by performing objectness-based bounding box regression analysis on the transformed feature map; and determining a refined bounding box based on the objectness probability map and the bounding box map.

Method and apparatus with object tracking

A processor-implemented object tracking method includes: setting a suppressed region in a template image based on a shape of a target box of the template image; refining a template feature map of the template image by suppressing an influence of feature data corresponding to the suppressed region in the template feature map; and tracking an object by determining a bounding box corresponding to the target box in a search image based on the refined template feature map.

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.

Method and apparatus with authentication and neural network training

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Suwon-si, South Korea

Ju Hwan Song