Abstract: A system of estimating image blur in digital images stored in a digital file. The disclosed system may calculate a blur value and direction of motion causing image blur based on detected edges within an input image and spectral energy information of the input image in the frequency domain.

Abstract: A server determines a plurality of faceprints representing a plurality of users to be recognized at a client device. Each faceprint contains a number of reference images for a given user that are used to recognize facial images of the user detecting in media captured at the client device. The faceprints delivered to the client device are determined for the client device based on the users likely to be detected in images captured at the client device. The reference images with a given faceprint delivered to the client device are selected by the server based on their recognition value in identifying the users likely to be detected in images captured at the client device.

Abstract: A method to recognize a facial image is described. An input facial image is normalized by scaling and rotation angle using methods of eye pupil centers detection. The input facial image is further normalized by lighting intensity. Template images are obtained either by the processing of certain images taken from different face positions, or by a preliminary reconstruction of a 3D face model based on stereo-pair images. Using the 3D model template, facial images are generated at different rotation angles. Distances between the input facial image and the template image are calculated from the Discrete Cosine Transformation (DCT) features, defined by overlapped blocks of these images. The facial image is recognized based on these distances.

Abstract: Image comparison techniques allow a quick method of recognizing and identifying faces or other objects appearing in images. A series of quick distance calculations can be performed between an unknown input image and a reference image. These calculations may include facial detection, normalization, discrete cosine transform calculations, and threshold comparisons to determine whether an image is recognized. In the case of identification uncertainty, slower but more precise motion aligned distance calculations are initiated. Motion aligned distance calculations involve generating a set of downscaled images, determining motion field and motion field-based distances between an unknown input image and reference image, best scale factors for aligning an unknown input image with reference images, and calculating affine transformation matrices to modify and align an unknown input image with reference images.

Abstract: Identification of facial images representing both animate and inanimate objects appearing in media, such as videos, may be performed using clustering. Clusters contain facial images representing the same or similar objects, providing a database for future automated facial image identification to be performed more quickly and easily. Clustering also allows videos or other media to be indexed so that segments that contain a certain object may be found without having to search through the entire length of the media. Clustering involves separating media data into individual frames and filtering for frames with facial images. A digital media processor may then process each facial image, compare it to other facial images, and form clusterizer tracks with the objective of forming a cluster. These newly formed clusters may be compared with previously formed clusters via key faces in order to determine the identity of facial images contained in the clusters.

Abstract: A method for processing digital media is described. The method, in one example embodiment, includes identification of objects in a video stream by detecting, for each video frame, an object in the video frame and selectively associating the object with an object cluster. The method may further include comparing the object in the object cluster to a reference object and selectively associating object data of the reference object with all objects within the object cluster based on the comparing. The method may further include manually associating the object data of the reference object with all objects within the object duster having no associated reference object and populating a reference database with the reference object for the object cluster.

Abstract: A method for processing digital media is described. The method, in one example embodiment, includes identification of objects in a video stream by detecting, for each video frame, an object in the video frame and selectively associating the object with an object cluster. The method may further include comparing the object in the object cluster to a reference object and selectively associating object data of the reference object with all objects within the object cluster based on the comparing. The method may further include manually associating the object data of the reference object with all objects within the object duster having no associated reference object and populating a reference database with the reference object for the object cluster.

Abstract: A method for processing digital media is described. The method, in one example embodiment, includes identification of objects in a video stream by detecting, for each video frame, an object in the video frame and selectively associating the object with an object cluster. The method may further include comparing the object in the object cluster to a reference object and selectively associating object data of the reference object with all objects within the object cluster based on the comparing. The method may further include manually associating the object data of the reference object with all objects within the object cluster having no associated reference object and populating a reference database with the reference object for the object cluster.

Abstract: A method to recognize a facial image is described. An input facial image is normalized by scaling and rotation angle using methods of eye pupil centers detection. The input facial image is further normalized by lighting intensity. Template images are obtained either by the processing of certain images taken from different face positions, or by a preliminary reconstruction of a 3D face model based on stereo-pair images. Using the 3D model template, facial images are generated at different rotation angles. Distances between the input facial image and the template image are calculated from the Discrete Cosine Transformation (DCT) features, defined by overlapped blocks of these images. The facial image is recognized based on these distances.

Abstract: Embodiments of computer implemented methods and systems for object clustering and identification are described. One example embodiment includes receiving an unclustered video object, determining a first distance between the unclustered video object and an arbitrary representative video object, the arbitrary representative video object being selected from representative video objects, estimating distances between the unclustered video object and the representative video objects based on the first distance and precalculated distances between the arbitrary representative video object and the representative video objects, and, based on the estimated distances, selectively associating the unclustered video object with a video cluster, thereby producing a clustered video object.

Abstract: A method to recognize a facial image is described. An input facial image is normalized by scaling and rotation angle using methods of eye pupil centers detection. The input facial image is further normalized by lighting intensity. Template images are obtained either by the processing of certain images taken from different face positions, or by a preliminary reconstruction of a 3D face model based on stereo-pair images. Using the 3D model template, facial images are generated at different rotation angles. Distances between the input facial image and the template image are calculated from the Discrete Cosine Transformation (DCT) features, defined by overlapped blocks of these images. The facial image is recognized based on these distances.

Abstract: A method for processing digital media is described. In one example embodiment, the method may include detecting an unknown object in a video frame, receiving inputs representing probable identities of the unknown object in the video frame from various sources, and associating each input with the unknown object detected in the video frame. The received inputs may be processed, compared with reference data and, based on the comparison, probable identities of the object associated with the input derived. The method may further include retrieving a likelihood of the input to match the unknown object from historical data and producing weights corresponding to the inputs, fusing the inputs and the relative weight associated with each input, and identifying the unknown object based on a comparison of the weighted distances from the unknown identify to a reference identity.

Abstract: A method to recognize a facial image is described. An input facial image is normalized by scaling and rotation angle using methods of eye pupil centers detection. The input facial image is further normalized by lighting intensity. Template images are obtained either by the processing of certain images taken from different face positions, or by a preliminary reconstruction of a 3D face model based on stereo-pair images. Using the 3D model template, facial images are generated at different rotation angles. Distances between the input facial image and the template image are calculated from the Discrete Cosine Transformation (DCT) features, defined by overlapped blocks of these images. The facial image is recognized based on these distances.