Abstract: Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with an autoencoder, a physical object in the identification region.

Abstract: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Abstract: Systems and techniques are provided for performing video-based activity recognition. For example, a process can include generating a three-dimensional (3D) model of a first portion of an object based on one or more frames depicting the object. The process can also include generating a mask for the one or more frames, the mask including an indication of one or more of regions of the object. The process can further include generating a 3D base model based on the 3D model of the first portion of the object and the mask, the 3D base model representing the first portion of the object and a second portion of the object. The process can include generating, based on the mask and the 3D base model, a 3D model of the second portion of the object.

Abstract: Techniques and systems are provided for processing one or more images. For example, a process can obtain a plurality of images captured during a session. The plurality of images include a current image of a current face. The process can extract features of the current face from the current image. The process can compare the extracted features of the current face to extracted features of a previous face from a previous image of the plurality of images captured during the session. The process can determine, based on comparing the extracted features of the current face to the extracted features of the previous face, whether the current face from the current image matches the previous face from the previous image. The process can determine whether to lock access to a computing device based on whether the current face from the current image matches the previous face from the previous image.

Abstract: Techniques are provided for generating three-dimensional models of objects from one or more images or frames. For example, at least one frame of an object in a scene can be obtained. A portion of the object is positioned on a plane in the at least one frame. The plane can be detected in the at least one frame and, based on the detected plane, the object can be segmented from the plane in the at least one frame. A three-dimensional (3D) model of the object can be generated based on segmenting the object from the plane. A refined mesh can be generated for a portion of the 3D model corresponding to the portion of the object positioned on the plane.

Abstract: Techniques are provided for generating one or more three-dimensional (3D) models. In one example, an image of an object (e.g., a face or other object) is obtained, and a 3D model of the object in the image is generated. The 3D model includes geometry information. Color information for the 3D model is determined, and a fitted 3D model of the object is generated based on a modification of the geometry information and the color information for the 3D model. In some cases, the color information (e.g., determination and/or modification of the color information) and the fitted 3D model can be based on one or more vertex-level fitting processes. A refined 3D model of the object is generated based on the fitted 3D model and depth information associated with the fitted 3D model. In some cases, the refined 3D model can be based on a pixel-level refinement or fitting process.

Abstract: A method performed by an electronic device is described. The method includes receiving first optical data and first depth data corresponding to a first frame. The method also includes registering the first depth data to a first canonical model. The method further includes fitting a three-dimensional (3D) morphable model to the first optical data. The method additionally includes registering the 3D morphable model to a second canonical model. The method also includes producing a 3D object reconstruction based on the registered first depth data and the registered 3D morphable model.

Abstract: A method performed by an electronic device is described. The method includes receiving first optical data and first depth data corresponding to a first frame. The method also includes registering the first depth data to a first canonical model. The method further includes fitting a three-dimensional (3D) morphable model to the first optical data. The method additionally includes registering the 3D morphable model to a second canonical model. The method also includes producing a 3D object reconstruction based on the registered first depth data and the registered 3D morphable model.

Abstract: A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.

Abstract: Methods, systems, and devices for image processing are described. The method may include identifying a face in a first image based on identifying one or more biometric features of the face, determining an angular direction of one or more pixels of the identified face, identifying an anchor point on the identified face, sorting each of one or more pixels of the identified face into one of a set of pixel bins based on a combination of the determined angular direction of the pixel and a distance between the pixel and the identified anchor point, and outputting an indication of authenticity associated with the face based on a number of pixels in each bin.

Abstract: A method performed by an electronic device is described. The method includes receiving an image. The image depicts a face. The method also includes detecting at least one facial landmark of the face in the image. The method further includes receiving a depth image of the face and determining at least one landmark depth by mapping the at least one facial landmark to the depth image. The method also includes determining a plurality of scales of depth image pixels based on the at least one landmark depth and determining a scale smoothness measure for each of the plurality of scales of depth image pixels. The method additionally includes determining facial liveness based on at least two of the scale smoothness measures. Determining the facial liveness may be based on a depth-adaptive smoothness threshold and/or may be based on a natural face size criterion.

Abstract: A method performed by an electronic device is described. The method includes receiving a set of frames. The set of frames describes a moving three-dimensional (3D) object. The method also includes registering the set of frames based on a canonical model. The canonical model includes geometric information and optical information. The method additionally includes fusing frame information of each frame to the canonical model based on the registration. The method further includes reconstructing the 3D object based on the canonical model.

Abstract: A method performed by an electronic device is described. The method includes obtaining a two-dimensional (2D) depth image. The method also includes extracting a 2D subset of the depth image. The 2D subset includes a center pixel and a set of neighboring pixels. The method further includes calculating a normal corresponding to the center pixel by calculating a covariance matrix based on the 2D subset.

Abstract: Methods, systems, and devices for image processing are described. The method may include identifying a face in a first image based on identifying one or more biometric features of the face, determining an angular direction of one or more pixels of the identified face, identifying an anchor point on the identified face, sorting each of one or more pixels of the identified face into one of a set of pixel bins based on a combination of the determined angular direction of the pixel and a distance between the pixel and the identified anchor point, and outputting an indication of authenticity associated with the face based on a number of pixels in each bin.

Abstract: A method performed by an electronic device is described. The method includes incrementally adding a current node to a graph. The method also includes incrementally determining a respective adaptive edge threshold for each candidate edge between the current node and one or more candidate neighbor nodes. The method further includes determining whether to accept or reject each candidate edge based on each respective adaptive edge threshold. The method additionally includes performing refining based on the graph to produce refined data. The method also includes producing a three-dimensional (3D) model based on the refined data.

Abstract: A method is described. The method includes determining normalized radiance of an image sequence based on a camera response function (CRF). The method also includes determining one or more reliability images of the image sequence based on a reliability function corresponding to the CRF. The method further includes extracting features based on the normalized radiance of the image sequence. The method additionally includes optimizing a model based on the extracted features and the reliability images.

Abstract: A method performed by an electronic device is described. The method includes receiving a set of frames. The set of frames describes a moving three-dimensional (3D) object. The method also includes registering the set of frames based on a canonical model. The canonical model includes geometric information and optical information. The method additionally includes fusing frame information of each frame to the canonical model based on the registration. The method further includes reconstructing the 3D object based on the canonical model.

Abstract: A depth based scanning system can be configured to determine whether pixel depth values of a depth map are within a depth range; determine a component of the depth map comprised of connected pixels each with a depth value within the depth range; replace the depth values of any pixels of the depth map that are not connected pixels; determine whether each pixel of the connected pixels of the component has at least a threshold number of neighboring pixels that have a depth value within the depth range; and for each pixel of the connected pixels of the component, if the pixel is determined to have at least the threshold number of neighboring pixels, replace its depth value with a filtered depth value that is based on the depth values of the neighboring pixels that have a depth value within the depth range.

Abstract: Techniques and systems are provided for performing object verification using radar images. For example, a first radar image and a second radar image are obtained, and features are extracted from the first radar image and the second radar image. A similarity is determined between an object represented by the first radar image and an object represented by the second radar image based on the features extracted from the first radar image and the features extracted from the second radar image. A determined similarity between these two sets of features is used to determine whether the object represented by the first radar image matches the object represented by the second radar image. Distances between the features in the two radar images can optionally also be compared and used to determine object similarity. The objects in the radar images may optionally be faces.

Abstract: Embodiments described herein can address these and other issues by using radar machine learning to address the radio frequency (RF) to perform object identification, including facial recognition. In particular, embodiments may obtain IQ samples by transmitting and receiving a plurality of data packets with a respective plurality of transmitter antenna elements and receiver antenna elements. I/Q samples indicative of a channel impulse responses of an identification region obtained from the transmission and reception of the plurality of data packets may then be used to identify, with an autoencoder, a physical object in the identification region.