Abstract:
A method and apparatus for generating a facial expression may receive an input image, and generate facial expression images that change from the input image based on an index indicating a facial expression intensity of the input image, the index being obtained from the input image
Abstract:
Provided is a method and apparatus to recognizing an object based on an attribute of the object and training that may calculate object age information from input data using an attribute layer trained with respect to an attribute of an object and a classification layer trained with respect to a classification of the object. The method to recognize the object includes extracting feature data from input data including an object using an object model, determining attribute classification information related to the input data from the feature data using a classification layer, determining attribute age information related to an attribute from the feature data using an attribute layer, and estimating object age information based on the attribute classification information and the attribute age information.
Abstract:
A liveness test method and apparatus is disclosed. A processor implemented liveness test method includes extracting an interest region of an object from a portion of the object in an input image, performing a liveness test on the object using a neural network model-based liveness test model, the liveness test model using image information of the interest region as provided first input image information to the liveness test model and determining liveness based at least on extracted texture information from the information of the interest region by the liveness test model, and indicating a result of the liveness test.
Abstract:
A recognizer training method and apparatus includes selecting training data, generating clusters by clustering the selected training data based on a global shape parameter, and classifying training data from at least one cluster based on a local shape feature.
Abstract:
A method and an apparatus for extracting a facial feature and a method and an apparatus for recognizing a face are provided, in which the apparatus for extracting a facial feature may extract facial landmarks from a current input image, sample a skin region and a facial component region based on the extracted facial landmarks, generate a probabilistic model associated with the sampled skin region and the facial component region, extract the facial component region from a face region included in the input image using the generated probabilistic model, and extract facial feature information from the extracted facial component region.
Abstract:
An image restoration method includes determining degradation information indicating a degradation factor of a degraded image, tuning the degradation information based on a tuning condition, and generating a restored image corresponding to the degraded image by executing an image restoration network with the degraded image and the degradation information.
Abstract:
A lightened neural network method and apparatus. The neural network apparatus includes a processor configured to generate a neural network with a plurality of layers including plural nodes by applying lightened weighted connections between neighboring nodes in neighboring layers of the neural network to interpret input data applied to the neural network, wherein lightened weighted connections of at least one of the plurality of layers includes weighted connections that have values equal to zero for respective non-zero values whose absolute values are less than an absolute value of a non-zero value. The lightened weighted connections also include weighted connections that have values whose absolute values are no greater than an absolute value of another non-zero value, the lightened weighted connections being lightened weighted connections of trained final weighted connections of a trained neural network whose absolute maximum values are greater than the absolute value of the other non-zero value.
Abstract:
Disclosed is a method and apparatus for generating an image. The apparatus includes at least one processor and a memory. The processor is configured to obtain a wide image of an entire region, obtain one or more teleimages of one or more regions of interest (ROIs) of the entire region using a telecamera according to a capturing order of the telecamera determined for the one or more ROIs based on the wide image, match the wide image and the one or more teleimages, warp the one or more teleimages to the wide image based on a result of the matching, and stitch the one or more warped teleimages based on the wide image.
Abstract:
Disclosed is a face verification method and apparatus. The method including analyzing a current frame of a verification image, determining a current frame state score of the verification image indicating whether the current frame is in a state predetermined as being appropriate for verification, determining whether the current frame state score satisfies a predetermined validity condition, and selectively, based on a result of the determining of whether the current frame state score satisfies the predetermined validity condition, extracting a feature from the current frame and performing verification by comparing a determined similarity between the extracted feature and a registered feature to a set verification threshold.
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.