Abstract: A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Abstract: Disclosed is an object recognition method and object recognition apparatus. The object recognition method may include detecting an object region comprising an object in an input image, determining scores corresponding to regions of the object region, determining weights based on the scores and detection information of the object region, determining a final score based on the scores and the determined weights, and determining a result of recognizing the object based on the final score.

Abstract: A device and method with object recognition is included. In one general aspect, an electronic device includes a camera sensor configured to capture a first image of a scene, the camera sensor is configured to perform at least one type of physical camera motion relative to the electronic device, the at least one type of physical camera motion includes rolling, panning, tilting, or zooming the camera sensor relative to the electronic device, and a processor configured to control the camera sensor to perform a physical motion of the physical camera motion type based on detecting an object in the first image, acquire a second image captured using the camera sensor as adjusted based on the performed physical motion, and recognize the object in the second image.

Abstract: A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Abstract: A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Abstract: An apparatus and method for training a neural network model for classification without a teacher model are disclosed. The includes: selecting classes from a database comprising a set of classes; generating a mean feature group comprising mean features extracted from the selected classes; receiving a batch comprising input data and extracting, by the neural network model, a feature from the input data, wherein the neural network model is to be trained according to a mean feature set; determining a first similarity between the extracted feature and a mean feature corresponding to the input data; determining a second similarity comprising a self-similarity of the mean feature; and updating a parameter of the neural network model based on the first similarity and the second similarity.

Abstract: A processor-implemented method with pose estimation includes: receiving an image, instance queries for object identification, and joint queries for joint identification; generating first heat maps associated with objects and second heat maps associated with joints in parallel based on the image, the instance queries, and the joint queries; and estimating respective joint information of a plurality of objects included in the image by combining the first heat maps and the second heat maps.

Abstract: A processor-implemented method includes generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image

Abstract: A processor-implemented method includes: generating a preprocessed infrared (IR) image by performing first preprocessing based on an IR image including an object; generating a preprocessed depth image by performing second preprocessing based on a depth image including the object; and determining whether the object is a genuine object based on the preprocessed IR image and the preprocessed depth image.

Abstract: Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Abstract: A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Abstract: A device and method with object recognition is included. In one general aspect, an electronic device includes a camera sensor configured to capture a first image of a scene, the camera sensor is configured to perform at least one type of physical camera motion relative to the electronic device, the at least one type of physical camera motion includes rolling, panning, tilting, or zooming the camera sensor relative to the electronic device, and a processor configured to control the camera sensor to perform a physical motion of the physical camera motion type based on detecting an object in the first image, acquire a second image captured using the camera sensor as adjusted based on the performed physical motion, and recognize the object in the second image.

Abstract: A liveness test method includes detecting a face region in an input image; generating, based on the detected face region, weight map data related to a face location in the input image; generating concatenated data by concatenating the weight map data with feature data generated from an intermediate layer of a liveness test model or image data of the input image; and generating a liveness test result based on a liveness score generated by the liveness test model provided with the concatenated data.

Abstract: A processor-implemented method with liveness detection includes: receiving a plurality of phase images of different phases; generating a plurality of preprocessed phase images by performing preprocessing, including edge enhancement processing, on the plurality of phase images of different phases; generating a plurality of differential images based on the preprocessed phase images; generating a plurality of low-resolution differential images having lower resolutions than the differential images, based on the differential images; generating a minimum map image based on the low-resolution differential images; and performing a liveness detection on an object in the phase images based on the minimum map image.

Abstract: Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Abstract: Disclosed is a method and apparatus for testing a liveness, where the liveness test method includes receiving a color image and a photodiode (PD) image of an object from an image sensor comprising a pixel formed of a plurality of PDs, preprocessing the color image and the PD image, and determining a liveness of the object by inputting a result of preprocessing the color image and a result of preprocessing the PD image into a neural network.

Abstract: A method with image recognition includes: extracting, using a feature extraction layer, feature data from an input image received by an image sensor; and outputting a recognition result of an object appearing in the input image, by applying a fixed mask and a variable mask to the extracted feature data, wherein the variable mask is adjusted in response to the extracted feature data.

Abstract: A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Abstract: A method and apparatus for detecting a liveness based on a phase difference are provided. The method includes generating a first phase image based on first visual information of a first phase, generating a second phase image based on second visual information of a second phase, generating a minimum map based on a disparity between the first phase image and the second phase image, and detecting a liveness based on the minimum map.

Abstract: A vehicle body structure for a vehicle having a battery and a rear seat includes a battery mounting portion provided on a floor in the rear of the rear seat and on which the battery is mounted, a cross member configured to reinforce the floor in the front of the battery mounting portion, a lower reinforcement portion configured to reinforce the floor below the battery mounting portion and to be supported on the cross member and both side members, and an upper fixing portion fastened to the lower reinforcement portion in a state of fixing the battery. The vehicle body structure is configured to protect the battery by absorbing and distributing the load from a collision, and effectively utilize the space of the rear side of the vehicle for installing the battery with the rear seat, etc.