Samsung Electronics Co., Ltd.

University of Southern California

Seoul National University

Seon Min Rhee

Jaejoon Han

Seungju Han

Jungbae Kim

Yong Beom Lee

Heewon Kim

Jihye Kim

Do Kyoon Kim

Byungin Yoo

Kyuhong Kim

Minsu Ko

Changkyu Choi

Tae Hyun Rhee

Ju Hwan Song

Hyong Euk Lee

Youngjun Kwak

Hana Lee

Byung In Yoo

Sang Wook Kim

Youngkyoo Hwang

Chang Kyu Choi

Hui Zhang

Sang-Wook Kim

Jongseok Kim

Kyung Hwan Kim

Minjung Kim

Seung Ju Han

Jae Joon Han

Youngsung Kim

Wonjun Hwang

Jiho Choi

Hyun Jung Shim

Yong-Il Lee

Paul E Debevec

Jung Bae Kim

Abhijeet Ghosh

Shandong Wang

Meng Ye

Won Jun Hwang

Jay Busch

Fei Duan

Wonsuk Chang

Yongjie Zhang

Gunhee Kim

Hui Li

Lujin Gong

Juhwan Song

Haoxing Wang

Jiguang Xue

Seung In Park

Jen-Yuan Chiang

Matt Chiang

Paul Devebec

Jaekyeom Kim

Dongyeon Woo

Young Jun Kwak

A method with face recognition includes: determining a first global feature of a first face image and a first global feature of a second face image based on a local feature of the first face image and a local feature of the second face image, respectively; determining a final global feature of the first face image based on the first global feature of the first face image and a second global feature of the first face image; determining a final global feature of the second face image based on the first global feature and a second global feature of the second face image; and recognizing the first face image and the second face image based on the final global feature of the first face image and the final global feature of the second face image, wherein the second global feature of the first face image is determined based on the local feature of the first face image, and the second global feature of the second face image is determined based on the local feature of the second face image.

Method and apparatus with face recognition

A method with image processing includes: generating a first surface normal image comprising surface normal vectors corresponding to pixels of a first depth image; and applying the first depth image and the first surface normal image to a first neural network, and acquiring a second depth image by changing the first depth image using the first neural network. The first neural network generates the second depth image to have an improved quality compared to the first depth image, based on an embedding vector that comprises a feature of the first depth image and a feature of the first surface normal image.

Method and apparatus with image processing for processing depth images

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

An image processing method includes receiving an input image and a guide image corresponding to the input image, extracting informative features from the input image and the guide image to enhance the input image, selectively obtaining a first feature for the input image from among the informative features, and processing the input image based on the first feature.

Image processing method and image processing apparatus

A method and apparatus for generating a depth image are provided. The apparatus receives an input image, extracts a feature corresponding to the input image, generates features for each depth resolution by decoding the feature using decoders corresponding to different depth resolutions, estimates probability distributions for each depth resolution by progressively refining the features for each depth resolution, and generates a target depth image corresponding to the input image based on a final estimated probability distribution from among the probability distributions for each depth resolution.

Method and apparatus for generating depth image

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 fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.

Method and apparatus with fingerprint verification

A neural network includes a drop layer configured to drops feature values. A method of computation using the neural network includes extracting feature data from input data using a first portion of a neural network, generating compressed representation data of the extracted feature data by dropping a feature value from the extracted feature data at a drop layer of the neural network based on a drop probability corresponding to the feature value, and indicating an inference result from the compressed representation data using a second portion of the neural network.

Apparatus and method with compressed neural network computation

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 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

A method of adaptively updating an enrollment database is disclosed. The method may include extracting a first feature vector from an input image, the input image including a face of a user, determining whether to enroll the input image in the enrollment database based on the first feature vector, second feature vectors of enrollment images and a representative vector, the second feature vectors of the enrollment images being enrolled in the enrollment database, and the representative vector representing the second feature vectors, and enrolling the input image in the enrollment database based on a result of the determining.

Methods and apparatuses for adaptively updating enrollment database for user authentication

Growing community of inventors

Seoul, South Korea

Seon Min Rhee