Abstract: 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

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method and apparatus with landmark coordinate prediction are provided. The method includes generating a multi-stage feature map for landmarks of a face image through a staged convolutional network, generating an initial query matrix by fully connecting a last-stage feature map in the multi-stage feature map using a fully connected network, where a total number of feature elements in the initial query matrix is equal to a total number of predicted landmarks of the face image, generating a memory feature matrix by flattening and connecting the multi-stage feature map, generating the predicted landmark coordinates by inputting the memory feature matrix and the initial query matrix to a decoder network of plural cascaded decoder networks.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A processor-implemented verification method includes: detecting a characteristic of an input image; acquiring input feature transformation data and enrolled feature transformation data by respectively transforming input feature data and enrolled feature data based on the detected characteristic, wherein the input feature data is extracted from the input image using a feature extraction model; and verifying a user corresponding to the input image based on a result of comparison between the input feature transformation data and the enrolled feature transformation data.

Abstract: Face recognition of a face, to determine whether the face correlates with an enrolled face, may include generating a personalized three-dimensional (3D) face model based on a two-dimensional (2D) input image of the face, acquiring 3D shape information and a normalized 2D input image of the face based on the personalized 3D face model, generating feature information based on the 3D shape information and pixel color values of the normalized 2D input image, and comparing the feature information with feature information associated with the enrolled face. The feature information may include first and second feature information generated based on applying first and second deep neural network models to the pixel color values of the normalized 2D input image and the 3D shape information, respectively. The personalized 3D face model may be generated based on transforming a generic 3D face model based on landmarks detected in the 2D input image.

Abstract: 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.

Abstract: 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.

Abstract: An apparatus for training an image generative model and an image generating apparatus are provided. The apparatus generates output images from a plurality of input images based on the generative model, extracts depth features from the respective output images based on a depth classification model, calculates a depth loss from the extracted depth features, and trains the generative model based on an overall loss that includes the calculated depth loss.