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 method of preprocessing an image including biological information is disclosed, in which an image preprocessor may set an edge line in an input image including biological information, calculate an energy value corresponding to the edge line, and adaptively crop the input image based on the energy value.

Abstract: A fingerprint recognition method includes determining a code corresponding to a query image based on features of blocks in the query image, obtaining information corresponding to the determined code from a lookup table, and verifying the query image based on the obtained information.

Abstract: At least one example embodiment discloses a method of extracting a feature from an input image. The method may include detecting landmarks from the input image, detecting physical characteristics between the landmarks based on the landmarks, determining a target area of the input image from which at least one feature is to be extracted and an order of extracting the feature from the target area based on the physical characteristics and extracting the feature based on the determining.

Abstract: Provided is a neural network method and apparatus. The method includes determining a target task with respect to input data, acquiring a second parameter that is prestored to correspond to the target task among first parameters included in a neural network for a plurality of tasks, adapting the neural network to the target task by setting a value of a portion of the first parameters of the neural network to a value of the second parameter, and implementing the adapted neural network with respect to the input data, and may include obtaining an importance matrix for neural network, determining one or more key parameters of the neural network, updating the importance matrix with respect to the determined one or more key parameters, and training the neural network with training data and for a new task using the updated importance matrix.

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: Disclosed is a user recognition apparatus and method, the apparatus configured to determine a validity of an iris area for each frame, to perform an iris recognition when the iris area is valid, and to extract a facial feature for a fusion recognition when the iris area is invalid.

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 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 fingerprint recognition method includes receiving an input partial image corresponding to a partial image of a fingerprint of a first user; partitioning the input partial image into a plurality of blocks; performing a comparison operation based on the plurality of blocks and the enrolled partial images corresponding to partial images of an enrolled fingerprint; and recognizing the fingerprint of the first user based on a result of the comparison operation.

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: 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 and apparatus for extracting an iris region is disclosed. The apparatus may generate a classification map associated with an iris region from an eye image using a trained neural network model, estimate a geometric parameter associated with the iris region using the generated classification map, and extract the iris region from the eye image based on the estimated geometric parameter.

Abstract: A user authentication method includes receiving a first input image including information on a first modality; receiving a second input image including information on a second modality; determining at least one first score by processing the first input image based on at least one first classifier, the at least one first classifier being based on the first modality; determining at least one second score by processing the second input image based on at least one second classifier, the at least one second classifier being based on the second modality; and authenticating a user based on the at least one first score, the at least one second score, a first fusion parameter of the at least one first classifier, and a second fusion parameter of the at least one second classifier.

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: An authentication method and corresponding apparatus includes obtaining iris images, and constituting an enroll set including iris codes and mask codes corresponding to the iris images. The authentication method and corresponding apparatus also include generating a synthesized code including a synthesized iris code and a synthesized mask code based on correlations between the iris codes included in the enroll set in block units.

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 method of converting a vector corresponding to an input image includes receiving input vector data associated with an input image including an object; and converting the received input vector data into feature data based on a projection matrix having a fixed rank, wherein a first dimension of the input vector data is higher than a second dimension of the feature data.

Abstract: A device and method of recognizing an iris is provided. An iris recognition device may determine a threshold brightness using a histogram of a reference area from an iris image and exclude a target pixel from iris recognition in a candidate area based on the determined threshold brightness.

Abstract: A liveness testing apparatus includes a testing circuit. The testing circuit is configured to test a liveness of an object included in a received input image based on whether an image of the object has a characteristic indicative of a flat surface or a characteristic indicative of a three-dimensional (3D) structure.