Abstract: The disclosed technology is generally directed to audio capture. In one example of the technology, recorded sounds are received such that the sounds recorded were emitted from multiple locations in an environment and such that the sounds recorded are sounds that can be converted to room impulse responses. The room impulse responses are generated from the recorded sounds. Location information that is associated with the multiple locations is received. At least the room impulses responses and the location information are used to generate at least one environment-specific model. Audio captured in the environment is received. An output is generated by processing the captured audio with the at least one environment-specific model such that the output includes at least one adjustment of the captured audio based on at least one acoustical property of the environment.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: Systems and methods for face recognition are provided. In one example, a method for face recognition includes receiving a user image and detecting a user luminance of data representing the user's face. An adaptive low pass filter is selected that corresponds to the user luminance of the user's face. The filter is applied to the user image to create a filtered user image. The filtered user image is projected to create a filtered user image representation. A filtered reference image representation that has been filtered with the same low pass filter is selected from a reference image database. The method then determines whether the filtered reference image representation matches the filtered user image representation.

Abstract: A social network application may identify images having common links between a first user's image collection and a second user's image collection. The common links may be identified through metadata or similar portions of the images. Using the first user's image collection, elements of interest may be identified and compared to a second user's image collection to find matches. When matches are found, the results may be selected from groups of results to show a diverse set of matches. The user may be presented with options to select and add matched images to the user's collection, as well as to browse more images that match one or more of the groups.

Abstract: A training set for a post-filter classifier is created from the output of a face detector. The face detector can be a Viola Jones face detector. Face detectors produce false positives and true positives. The regions in the training set are labeled so that false positives are labeled negative and true positives are labeled positive. The labeled training set is used to train a post-filter classifier. The post-filter classifier can be an SVM (Support Vector Machine). The trained face detection classifier is placed at the end of a face detection pipeline comprising a face detector, one or more feature extractors and the trained post-filter classifier. The post-filter reduces the number of false positives in the face detector output while keeping the number of true positives almost unchanged using features different from the Haar features used by the face detector.

Abstract: A system may recognize faces within an image by using wireless identifiers captured at the time the image was taken to determine a list of candidates for facial recognition. A database may contain people associated with one or more wireless identifiers, which may be identifiers associated with various protocols, such as Bluetooth, cellular telephones, WiFi, or other protocols. In some cases, the list of candidates may be expanded by using candidate's social networks. The recognized faces may be tagged in the image as metadata, then used in various scenarios. In one scenario, an album of images from an event may be created by matching people who were tagged in images. In another scenario, people may exchange business contact information or social network contacts by taking images of each other.

Abstract: Systems and methods for face recognition are provided. In one example, a method for face recognition includes receiving a user image and detecting a user luminance of data representing the user's face. An adaptive low pass filter is selected that corresponds to the user luminance of the user's face. The filter is applied to the user image to create a filtered user image. The filtered user image is projected to create a filtered user image representation. A filtered reference image representation that has been filtered with the same low pass filter is selected from a reference image database. The method then determines whether the filtered reference image representation matches the filtered user image representation.

Abstract: A video previewing and selection system may allow a user to filter and select video clips based on metadata associated with the video clips, including metadata that defines when certain people are shown in the clips. A set of video sequences may be analyzed to extract existing metadata and present the metadata in a user interface. A user may select various metadata which may be used to filter the selections using logical AND and OR combinations. The user interface may allow a user to view the clips, as well as select clips for further editing by a video editor.

Abstract: A system may recognize faces within an image by using wireless identifiers captured at the time the image was taken to determine a list of candidates for facial recognition. A database may contain people associated with one or more wireless identifiers, which may be identifiers associated with various protocols, such as Bluetooth, cellular telephones, WiFi, or other protocols. In some cases, the list of candidates may be expanded by using candidate's social networks. The recognized faces may be tagged in the image as metadata, then used in various scenarios. In one scenario, an album of images from an event may be created by matching people who were tagged in images. In another scenario, people may exchange business contact information or social network contacts by taking images of each other.

Abstract: A facial detecting system may analyze a video by traversing the video forwards and backwards to create tracks of a person within the video. After separating the video into shots, the frames of each shot may be analyzed using a face detector algorithm to produce some analyzed information for each frame. A facial track may be generated by grouping the faces detected and by traversing the sequence of frames forwards and backwards. Facial tracks may be joined together within a shot to generate a single track for a person's face within the shot, even when the tracks are discontinuous.

Abstract: A training set for a post-filter classifier is created from the output of a face detector. The face detector can be a Viola Jones face detector. Face detectors produce false positives and true positives. The regions in the training set are labeled so that false positives are labeled negative and true positives are labeled positive. The labeled training set is used to train a post-filter classifier. The post-filter classifier can be an SVM (Support Vector Machine). The trained face detection classifier is placed at the end of a face detection pipeline comprising a face detector, one or more feature extractors and the trained post-filter classifier. The post-filter reduces the number of false positives in the face detector output while keeping the number of true positives almost unchanged using features different from the Haar features used by the face detector.

Abstract: Images from two image databases may be correlated based on identifying a common event, which may be determined by image metadata as well as image content. The image metadata may include timestamps, geotagging metadata, or other tags, as well as input from a social network application in some embodiments. The image content may include analysis to find common persons based on facial recognition or color histograms, common background components, or other common features. The common event may be used to identify images that may be shared among the participants of the event by a social network application, as well as other purposes.

Abstract: A user's collection of images may be analyzed to identify people's faces within the images, then create clusters of similar faces, where each of the clusters may represent a person. The clusters may be ranked in order of size to determine a relative importance of the associated person to the user. The ranking may be used in many social networking applications to filter and present content that may be of interest to the user. In one use scenario, the clusters may be used to identify images from a second user's image collection, where the identified images may be pertinent or interesting to the first user. The ranking may also be a function of user interactions with the images, as well as other input not related to the images. The ranking may be incrementally updated when new images are added to the user's collection.

Abstract: A social network application may identify images having common links between a first user's image collection and a second user's image collection. The common links may be identified through metadata or similar portions of the images. Using the first user's image collection, elements of interest may be identified and compared to a second user's image collection to find matches. When matches are found, the results may be selected from groups of results to show a diverse set of matches. The user may be presented with options to select and add matched images to the user's collection, as well as to browse more images that match one or more of the groups.

Abstract: Systems and methods are described herein for finding and sharing digital images of a user, such as digital photographs of the user, that are located in collections of digital images belonging to others. In accordance with at least one implementation, a face model of a first user is built using a first user computer, wherein the face model is built based on digital images of the first user stored on or accessible to the first user computer. The face model of the first user is then made accessible to a second user computer for use by the second user computer in finding digital images of the first user stored on or accessible to the second user computer. The digital images found by the second user computer are then made accessible to the first user computer.

Abstract: An application management system may have a user interface in which a user may input a text phrase that describes a desired action. The system may generate metadata relating to the text phrase and distribute the metadata and text phrase to many different registered applications, some of which may be web based applications. Each application may return one or more suggested actions, along with some optional information from the application. The suggested actions may be ranked and presented on the user interface, and a user may select an action to be performed. The system may launch the application and have the action performed.