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:
At least one example embodiment discloses a method of extracting a feature of an input image. The method includes constructing an example pyramid including at least one hierarchical level based on stored example images, generating a codebook in each of the at least one hierarchical level, calculating a similarity between the codebook and the input image, and extracting a feature of the input image based on the similarity.
Abstract:
A method and apparatus for processing an image is disclosed, wherein the apparatus for processing the image may set blocks in an input image, perform an orthogonal transform on pixel values in the blocks, obtain orthogonal transform coefficients, and generate a resulting image by normalizing the obtained orthogonal transform coefficients.
Abstract:
A method and device with transferal of reinforcement learning are disclosed. The method includes: approximating an optimal value-function for a task vector using a value approximator trained to output a minimum value-function using a state of an agent and source task vectors; determining an upper bound of the optimal value-function for the task vector; determining a lower bound of the optimal value-function for the task vector; correcting the optimal value-function for the task vector based on the upper bound and the lower bound; and determining an optimal policy for the task vector using the corrected optimal value-function for the task vector.
Abstract:
A method of estimating depth information includes generating a first simulated image using a simulator provided with a first depth map, training an artificial neural network model based on the first depth map and the first simulated image, generating a second depth map by inputting an actual image into the trained artificial neural network model, and generating a second simulated image using the simulator provided with the second depth map.
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 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 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:
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 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.