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.

Abstract: A method and an apparatus for registering a face, and a method and an apparatus for recognizing a face are disclosed, in which a face registering apparatus may change a stored three-dimensional (3D) facial model to an individualized 3D facial model based on facial landmarks extracted from two-dimensional (2D) face images, match the individualized 3D facial model to a current 2D face image of the 2D face images, and extract an image feature of the current 2D face image from regions in the current 2D face image to which 3D feature points of the individualized 3D facial model are projected, and a face recognizing apparatus may perform facial recognition based on image features of the 2D face images extracted by the face registering apparatus.

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.

Abstract: A method and an apparatus for registering a face, and a method and an apparatus for recognizing a face are disclosed, in which a face registering apparatus may change a stored three-dimensional (3D) facial model to an individualized 3D facial model based on facial landmarks extracted from two-dimensional (2D) face images, match the individualized 3D facial model to a current 2D face image of the 2D face images, and extract an image feature of the current 2D face image from regions in the current 2D face image to which 3D feature points of the individualized 3D facial model are projected, and a face recognizing apparatus may perform facial recognition based on image features of the 2D face images extracted by the face registering apparatus.

Abstract: A fingerprint verification method and a fingerprint verification apparatus performing the fingerprint verification method are disclosed. The fingerprint verification apparatus determines a first similarity between a query fingerprint image and each of registered fingerprint images, selects a target registered fingerprint image group from registered fingerprint image groups based on the first similarity, determines a second similarity between the query fingerprint image and each of registered fingerprint images in the target registered fingerprint image group based on matching relationship information between the registered fingerprint images in the target registered fingerprint image group, and determines whether fingerprint verification of the query fingerprint image is successful based on the second similarity.

Abstract: An apparatus and corresponding method are provided to match images and include assigning depth candidate values to a pixel in a first image, and reassigning third depth candidate values to a first pixel in the first image based on first depth candidate values assigned to the first pixel and second depth candidate values assigned to a second pixel adjacent to the first pixel. The apparatus and method also include determining one of the third depth candidate values to be a depth value of the first pixel, and matching the first pixel and a third pixel in a second image corresponding to the determined depth value of the first pixel.

Abstract: A fingerprint verification method and apparatus is disclosed. The fingerprint verification method may include obtaining an input fingerprint image, determining a matching region between the input fingerprint image and a registered fingerprint image, determining a similarity corresponding to the matching region, representing a determined indication of similarities between the input fingerprint image and the registered fingerprint image, relating the determined similarity to the matching region as represented in a matching region-based similarity, determining a result of a verification of the input fingerprint image based on the matching region-based similarity, and indicating the result of the verification.

Abstract: A method of selecting a candidate fingerprint image for fingerprint recognition includes acquiring a target fingerprint image, generating a target frequency image based on a frequency conversion of the target fingerprint image, extracting frequency feature information associated with a representative frequency from the target frequency image, and selecting a candidate fingerprint image from enrolled fingerprint images by comparing the frequency feature information to enrolled frequency information.

Abstract: A method of generating a three-dimensional (3D) face model includes extracting feature points of a face from input images comprising a first face image and a second face image; deforming a generic 3D face model to a personalized 3D face model based on the feature points; projecting the personalized 3D face model to each of the first face image and the second face image; and refining the personalized 3D face model based on a difference in texture patterns between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected.

Abstract: An apparatus for estimating a camera pose includes an image acquisition unit to acquire a photographed image, a motion sensor to acquire motion information of the apparatus for estimating the camera pose, a static area detector to detect a static area of the photographed image based on the photographed image and the motion information, and a pose estimator to estimate a camera pose based on the detected static area.

Abstract: Provided is a camera pose estimation apparatus that estimates an initial camera pose using one of an input depth image and an input color image, and refines the initial camera pose using the other image. When the initial camera pose is estimated using the input depth image, the radius of a first area, in which color information is matched for refinement, can be adaptively set according to the distribution of the depth value of at least one first point that is subject to matching.

Abstract: A method of generating a three-dimensional (3D) face model includes extracting feature points of a face from input images comprising a first face image and a second face image; deforming a generic 3D face model to a personalized 3D face model based on the feature points; projecting the personalized 3D face model to each of the first face image and the second face image; and refining the personalized 3D face model based on a difference in texture patterns between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected.

Abstract: A method of generating a three-dimensional (3D) face model includes extracting feature points of a face from input images comprising a first face image and a second face image; deforming a generic 3D face model to a personalized 3D face model based on the feature points; projecting the personalized 3D face model to each of the first face image and the second face image; and refining the personalized 3D face model based on a difference in texture patterns between the first face image to which the personalized 3D face model is projected and the second face image to which the personalized 3D face model is projected.

Abstract: An adaptive updating method of an enrollment database is disclosed. The method may include extracting a first feature vector from an input image of a user, determining whether the input image is to be enrolled in an enrollment database based on the first feature vector, second feature vectors of enrollment images including initial enrollment images enrolled in the enrollment database, and a representative vector representing the initial enrollment images, and enrolling the input image in the enrollment database based on a result of the determining.

Abstract: A three-dimensional (3D) face modeling method and apparatus is disclosed. The 3D face modeling apparatus may generate a personalized 3D face model using a two-dimensional (2D) input image and a generic 3D face model, obtain a depth image and a texture image using the generated personalized 3D face model, determine a patch region of each of the depth image and the texture image, and adjust a shape of the personalized 3D face model based on a matching relationship between the patch region of the depth image and the patch region of the texture image.

Abstract: A method and an apparatus for recognizing an object are disclosed. The apparatus may extract a plurality of features from an input image using a single recognition model and recognize an object in the input image based on the extracted features. The single recognition model may include at least one compression layer configured to compress input information and at least one decompression layer configured to decompress the compressed information to determine the features.

Abstract: A method of adaptively updating an enrollment database is disclosed. The method may include extracting a first feature vector from an input image, the input image including a face of a user, determining whether to enroll the input image in the enrollment database based on the first feature vector, second feature vectors of enrollment images and a representative vector, the second feature vectors of the enrollment images being enrolled in the enrollment database, and the representative vector representing the second feature vectors, and enrolling the input image in the enrollment database based on a result of the determining.

Abstract: A method of controlling an electronic device having a transparent display. The method includes controlling the electronic device to provide a first image through an optical display mode operation of the transparent display in which light incident from an object is transmitted, controlling the electronic device to acquire a second image through a video display mode operation of the transparent display in which an image captured from the object is displayed, and selectively displaying the first and second images using the transparent display by a mixing, by the electronic device, of the first and second images. The mixing may be dependent on a distance between the electronic device and the object or a display ratio between the first and second images. An image for an object may be displayed in a region of interest and an image for another object in a remaining region.

Abstract: A method and apparatus for detecting a three-dimensional (3D) point cloud point of interest (POI), the apparatus comprising a 3D point cloud data acquirer to acquire 3D point cloud data, a shape descriptor to generate a shape description vector describing a shape of a surface in which a pixel point of a 3D point cloud and a neighboring point of the pixel point are located, and a POI extractor to extract a POI based on the shape description vector is disclosed.

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.