Abstract: Provided is a user authentication method and apparatus that obtains first environmental information indicating an environmental condition in which an input image of a user is captured, extracts a first feature vector from the input image, selects a second feature vector including second environmental information that matches the first environmental information from enrolled feature vectors in an enrollment database (DB), and authenticates the user based on the first feature vector and the second feature vector.

Abstract: A method to analyze a facial image includes: inputting a facial image to a residual network including residual blocks that are sequentially combined and arranged in a direction from an input to an output; processing the facial image using the residual network; and acquiring an analysis map from an output of an N-th residual block among the residual blocks using a residual deconvolution network, wherein the residual network transfers the output of the N-th residual block to the residual deconvolution network, and N is a natural number that is less than a number of all of the residual blocks, and wherein the residual deconvolution network includes residual deconvolution blocks that are sequentially combined, and the residual deconvolution blocks correspond to respective residual blocks from a first residual block among the residual blocks to the N-th residual block.

Abstract: A lightened neural network, method, and apparatus, and recognition method and apparatus implementing the same. A neural network includes a plurality of layers each comprising neurons and plural synapses connecting neurons included in neighboring layers. Synaptic weights with values greater than zero and less than a preset value of a variable a, which is greater than zero, may be at least partially set to zero. Synaptic weights with values greater than a preset value of a variable b, which is greater than zero, may be at least partially set to the preset value of the variable b.

Abstract: A content recommendation method and device for recommending content to a user are disclosed. According to one embodiment, the content recommendation device extracts the features of a user from image data, audio data and the like, and can determine a recognition rate indicating the degree that is recognized as a user model predetermined according to the features of the user. The content recommendation device can determine the recommended content to be provided to the user on the basis of the determined recognition rate.

Abstract: A lightened neural network, method, and apparatus, and recognition method and apparatus implementing the same. A neural network includes a plurality of layers each comprising neurons and plural synapses connecting neurons included in neighboring layers. Synaptic weights with values greater than zero and less than a preset value of a variable a, which is greater than zero, may be at least partially set to zero. Synaptic weights with values greater than a preset value of a variable b, which is greater than zero, may be at least partially set to the preset value of the variable b.

Abstract: Provided is a liveness verification method and device. A liveness verification device acquires a first image and a second image, and select one or more liveness models based on respective analyses of the first image and the second image, including analyses based on an object part being detected in the first image and/or the second image, and to verify, using the selected one or more liveness models, a liveness of the object based on the first image and/or the second image. The first image may be a color image and the second image may be an Infrared image.

Abstract: A processing method using a neural network includes generating output maps of a current layer of the neural network by performing a convolution operation between input maps of the current layer and weight kernels of the current layer, determining a lightweight format for the output maps of the current layer based on a distribution of at least a portion of activation data being processed in the neural network, and lightening activation data corresponding to the output maps of the current layer to have a low bit width based on the determined lightweight format.

Abstract: The facial verification apparatus is a mobile computing apparatus, including a camera to capture an image, a display, and one or more processors. While in a lock state, the image is captured and facial verification performed using a face image, or using a detected face and in response to the face being detected. The facial verification includes a matching with respect to the detected face, or obtained face image, and a registered face information. If the verification is successful, the lock state of the apparatus may be canceled and the user allowed access to the apparatus. The lock state may be cancelled when the verification is successful and the user has been determined to have been attempting to gain access to the apparatus. Face image feedback to the user may not be displayed during the detecting for, or obtaining of, the face and/or performing of the facial verification.

Abstract: A method and apparatus of generating a three-dimensional (3D) image are provided. The method of generating a 3D image involves acquiring a plurality of images of a 3D object with a camera, calculating pose information of the plurality of images based on pose data for each of the plurality of images measured by an inertial measurement unit, and generating a 3D image corresponding to the 3D object based on the pose information.

Abstract: An object recognition apparatus, a classification tree learning apparatus, an operation method of the object recognition apparatus, and an operation method of the classification tree learning apparatus are provided. The object recognition apparatus may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, using a classification tree.

Abstract: A three-dimensional display device and a user interfacing method therefor are disclosed. The three-dimensional display device according to one embodiment comprises: a display unit for displaying a three-dimensional virtual object; a user input signal generation unit for generating a user input signal by detecting a handling object for handling an operation of the three-dimensional virtual object in a three-dimensional space matched with the three-dimensional virtual object; and a control unit for controlling the operation of the three-dimensional virtual object according to the user input signal.

Abstract: Provided is a user authentication method and apparatus that obtains first environmental information indicating an environmental condition in which an input image of a user is captured, extracts a first feature vector from the input image, selects a second feature vector including second environmental information that matches the first environmental information from enrolled feature vectors in an enrollment database (DB), and authenticates the user based on the first feature vector and the second feature vector.

Abstract: An object recognition system is provided. The object recognition system for recognizing an object may include an input unit to receive, as an input, a depth image representing an object to be analyzed, and a processing unit to recognize a visible object part and a hidden object part of the object, from the depth image, by using a classification tree. The object recognition system may include a classification tree learning apparatus to generate the classification tree.

Abstract: A method and corresponding apparatus include extracting a movement trajectory feature of an object from an input video. The method and corresponding apparatus also include coding the extracted movement trajectory feature, and determining a type of a movement of the object based on the coded movement trajectory feature.

Abstract: A method and an apparatus for adjusting a pose in a face image are provided. The method of adjusting a pose in a face image involves detecting two-dimensional (2D) landmarks from a 2D face image, positioning three-dimensional (3D) landmarks in a 3D face model by determining an initial pose of the 3D face model based on the 2D landmarks, updating the 3D landmarks by iteratively adjusting a pose and a shape of the 3D face model, and adjusting a pose in the 2D face image based on the updated 3D landmarks.

Abstract: An interfacing device for providing a user interface (UI) exploiting a multi-modality may recognize at least two modality inputs for controlling a scene, and generate scene control information based on the at least two modality inputs.

Abstract: A face verification method and apparatus is disclosed. The face verification method includes selecting a current verification mode, from among plural verification modes, to be implemented for the verifying of the face, determining one or more recognizers, from among plural recognizers, based on the selected current verification mode, extracting feature information from information of the face using at least one of the determined one or more recognizers, and indicating whether a verification is successful based on the extracted feature information.

Abstract: A facial verification method and apparatus is disclosed. The facial verification method includes detecting a face region in an input image, determining whether the detected face region represents a partial face, in response to a determination that the detected face region represents the partial face, generating a synthesized image by combining image information of the detected face region and reference image information, performing a verification operation with respect to the synthesized image and predetermined first registration information, and indicating whether facial verification of the input image is successful based on a result of the performed verification operation.

Abstract: Disclosed is a liveness test method and apparatus. A liveness test apparatus determines a pre-liveness score based on a plurality of sub-images acquired from an input image, determines a post-liveness score based on a recognition model for recognizing an object included in the input image, and determines a liveness of the object based on any one or any combination of the pre-liveness score and the post-liveness score.

Abstract: An image processing method implemented by a processor includes receiving an image, acquiring a target image that includes an object from the image, calculating an evaluation score by evaluating a quality of the target image, and detecting the object from the target image based on the evaluation score.