Abstract: An image processing method includes extracting a first region in a first image by inputting the first image to a pretrained neural network, upscaling a resolution of the first region by performing neural network-based super resolution processing on the first region, and upscaling a resolution of a second region in the first image from which the first region is excluded by performing interpolation on the second region.

Abstract: An image processing method includes extracting a first region in a first image by inputting the first image to a pretrained neural network, upscaling a resolution of the first region by performing neural network-based super resolution processing on the first region, and upscaling a resolution of a second region in the first image from which the first region is excluded by performing interpolation on the second region.

Abstract: A user verification apparatus may perform user verification using multiple biometric verifiers. The user verification apparatus may set a termination stage of one or more biometric verifiers. Multiple biometric verifiers may be used to generate outputs, for which separate termination stages are set to establish a particular combination of set termination stages associated with the multiple biometric verifiers, and the user verification apparatus may fuse outputs of the biometric verifiers based on the particular combination of set termination stages. The user verification apparatus may verify a user based on a result of the fusing, and an unlocking command signal may be generated based on the verifying. The unlocking command signal may be generated to selectively grant access, to the verified user, to one or more elements of a device. The device may be a vehicle.

Abstract: A fingerprint verification method includes selecting one or more first fingerprint groups from among a plurality of fingerprint groups based on an input fingerprint image, each fingerprint group of the plurality of fingerprint groups including partial fingerprint images; and determining whether verification is successful based on the input fingerprint image and each of the partial fingerprint images included in the one or more first fingerprint groups.

Abstract: A method for updating biometric authentication data authenticates an input image using an enrollment database (DB) over a first length of time, the authentication including generating information for authenticating the input image, and updates the enrollment DB based on the first length time and the information for authenticating the input image.

Abstract: An electronic device includes: a state observer configured to observe a state of the electronic device according to an environment interactable with the electronic device; one or more processors configured to: determine a skill based on the observed state; determine a goal based on the determined skill and the observed state; and determine, based on the state and the determined goal, an action causing a linear state transition of the electronic device in a direction toward the determined goal in a state space; and a controller configured to control an operation of the electronic device based on the determined action.

Abstract: A processor-implemented method includes: generating a first sample image and a second sample image by performing data augmentation on an input training image; generating a first feature map of the first sample image and a second feature map of the second sample image by performing feature extraction on the first sample image and the second sample image using an encoding model; determining first loss data according to a relationship between first feature vectors of the first feature map and second feature vectors of the second feature map; estimating relative geometric information of the first feature map and the second feature map using a relationship estimation model; determining second loss data according to the relative geometric information, based on label data according to a geometric arrangement of the first sample image and the second sample image in the input training image; and training the encoding model and the relationship estimation model, based on the first loss data and the second loss data.

Abstract: Training of a machine learning model is included. An electronic device includes a memory storing a machine learning model including a discriminator model, and a processor configured to extract, from a training image, a first image patch and a second image patch at least partially overlapping the first image patch, extract, from the first image patch, a first feature map based on a layer of the discriminator model, extract, from the second image patch, a second feature map based on the layer, extract a first partial feature map from a projected map that is projected based on the first feature map, and train the discriminator model based on a first objective function value generated based on a second partial feature map and the first partial feature map, wherein the second partial feature map corresponds to a part of the extracted second feature map.

Abstract: A method and apparatus with fingerprint verification are disclosed. A processor implemented method includes obtaining a query fingerprint image through a fingerprint sensor, obtaining a processed fingerprint image by performing image processing on the obtained query fingerprint image, obtaining a blended fingerprint image by blending the query fingerprint image and the processed fingerprint image, and performing fingerprint verification based on the blended fingerprint image and a registered fingerprint image.

Abstract: A method with generation of a transformed image includes: receiving an input image; extracting, from the input image, coefficients corresponding to semantic elements of the input image; selecting at least one first target coefficient, among the coefficients, corresponding to at least one target semantic element that is to be changed among the semantic elements of the input image; changing the at least one first target coefficient; and generating a transformed image from the input image by applying the coefficients, including the changed at least one first target coefficient, to basis vectors used to represent the semantic elements of the input image in an embedding space of a neural network, the basis vectors corresponding to the semantic elements of the input image.

Abstract: A fingerprint verification method includes selecting one or more first fingerprint groups from among a plurality of fingerprint groups based on an input fingerprint image, each fingerprint group of the plurality of fingerprint groups including partial fingerprint images; and determining whether verification is successful based on the input fingerprint image and each of the partial fingerprint images included in the one or more first fingerprint groups.

Abstract: A fingerprint verification method includes selecting one or more first fingerprint groups from among a plurality of fingerprint groups based on an input fingerprint image, each fingerprint group of the plurality of fingerprint groups including partial fingerprint images; and determining whether verification is successful based on the input fingerprint image and each of the partial fingerprint images included in the one or more first fingerprint groups.

Abstract: A method with generation of a transformed image includes: receiving an input image; extracting, from the input image, coefficients corresponding to semantic elements of the input image; selecting at least one first target coefficient, among the coefficients, corresponding to at least one target semantic element that is to be changed among the semantic elements of the input image; changing the at least one first target coefficient; and generating a transformed image from the input image by applying the coefficients, including the changed at least one first target coefficient, to basis vectors used to represent the semantic elements of the input image in an embedding space of a neural network, the basis vectors corresponding to the semantic elements of the input image.

Abstract: An apparatus and method for detecting a fake fingerprint is disclosed. The apparatus may divide an input fingerprint image into blocks, determine an image quality assessment (IQA) value associated with each block, determine a confidence value based on the IQA values using a confidence determination model, and determine whether an input fingerprint in the input fingerprint image is a fake fingerprint based on the determined confidence value.

Abstract: A method and apparatus with fingerprint verification are disclosed. A processor implemented method includes obtaining a query fingerprint image through a fingerprint sensor, obtaining a processed fingerprint image by performing image processing on the obtained query fingerprint image, obtaining a blended fingerprint image by blending the query fingerprint image and the processed fingerprint image, and performing fingerprint verification based on the blended fingerprint image and a registered fingerprint image.

Abstract: A user recognition method and apparatus, the user recognition method including performing a liveness test by extracting a first feature of a first image acquired by capturing a user, and recognizing the user by extracting a second feature of the first image based on a result of the liveness test, is provided.

Abstract: A method of recognizing a feature of an image may include receiving an input image including an object; extracting first feature information using a first layer of a neural network, the first feature information indicating a first feature corresponding to the input image among a plurality of first features; extracting second feature information using a second layer of the neural network, the second feature information indicating a second feature among a plurality of second features, the indicated second feature corresponding to the first feature information; and recognizing an element corresponding to the object based on the first feature information and the second feature information.

Abstract: A user verification apparatus may perform user verification using multiple biometric verifiers. The user verification apparatus may set a termination stage of one or more biometric verifiers. Multiple biometric verifiers may be used to generate outputs, for which separate termination stages are set to establish a particular combination of set termination stages associated with the multiple biometric verifiers, and the user verification apparatus may fuse outputs of the biometric verifiers based on the particular combination of set termination stages. The user verification apparatus may verify a user based on a result of the fusing, and an unlocking command signal may be generated based on the verifying. The unlocking command signal may be generated to selectively grant access, to the verified user, to one or more elements of a device. The device may be a vehicle.

Abstract: A user verification apparatus may perform user verification using multiple biometric verifiers. The user verification apparatus may set a termination stage of one or more biometric verifiers. Multiple biometric verifiers may be used to generate outputs, for which separate termination stages are set to establish a particular combination of set termination stages associated with the multiple biometric verifiers, and the user verification apparatus may fuse outputs of the biometric verifiers based on the particular combination of set termination stages. The user verification apparatus may verify a user based on a result of the fusing, and an unlocking command signal may be generated based on the verifying. The unlocking command signal may be generated to selectively grant access, to the verified user, to one or more elements of a device. The device may be a vehicle.

Abstract: An image processing method includes extracting a first region in a first image by inputting the first image to a pretrained neural network, upscaling a resolution of the first region by performing neural network-based super resolution processing on the first region, and upscaling a resolution of a second region in the first image from which the first region is excluded by performing interpolation on the second region.