Abstract: Images of a hand are obtained by a camera. These images may depict the fingers and palm of the user. A pose of the hand relative to the camera may vary due to rotation, translation, articulation of joints in the hand, and so forth. One or more canonical images are generated by mapping the images to a canonical model. A first embedding model is used to determine a first embedding vector representative of the palm as depicted in the canonical images. A second embedding model is used to determine a set of second embedding vectors, each representative of individual fingers as depicted in the canonical images. Embedding distances in the embedding space from the embedding vectors to a closest match of previously stored embedding vectors are multiplied together to determine an overall distance. If the overall distance is less than a threshold value, an identity of a user is asserted.

Abstract: A machine learning system to determine an identity of a user is trained using triplets of ad hoc synthetic data and actual data. The data may comprise multimodal images of a hand. Each triplet comprises an anchor, a positive, and a negative image. Synthetic triplets for different synthesized identities are generated on an ad hoc basis and provided as input during training of the machine learning system. The machine learning system uses a pairwise label-based loss function, such as a triplet loss function during training. Synthetic triplets may be generated to provide more challenging training data, to provide training data for categories that are underrepresented in the actual data, and so forth. The system uses substantially less memory during training, and the synthetic triplets need not be retained further reducing memory use. Ongoing training is supported as new actual triplets become available, and may be supplemented by additional synthetic triplets.

Abstract: A scanner acquires a set of images of a hand of a user to facilitate identification. These images may vary, due to changes in relative position, pose, lighting, obscuring objects such as a sleeve, and so forth. A first neural network determines output data comprising a spatial mask and a feature map for individual images in the set. The output data for two or more images is combined to provide aggregate data that is representative of the two or more images. The aggregate data may then be processed using a second neural network, such as convolutional neural network, to determine an embedding vector. The embedding vector may be stored and associated with a user account. At a later time, images acquired from the scanner may be processed to produce an embedding vector that is compared to the stored embedding vector to identify a user at the scanner.

Abstract: During a training phase, a first machine learning system is trained using actual data, such as multimodal images of a hand, to generate synthetic image data. During training, the first system determines latent vector spaces associated with identity, appearance, and so forth. During a generation phase, latent vectors from the latent vector spaces are generated and used as input to the first machine learning system to generate candidate synthetic image data. The candidate image data is assessed to determine suitability for inclusion into a set of synthetic image data that may be used for subsequent use in training a second machine learning system to recognize an identity of a hand presented by a user. For example, the candidate synthetic image data is compared to previously generated synthetic image data to avoid duplicative synthetic identities. The second machine learning system is then trained using the approved candidate synthetic image data.

Abstract: Described is a method for processing image data to determine if a portion of the imaged environment is exposed to high illumination, such as sunlight. In some implementations, image data from multiple different imaging devices may be processed to produce for each imaging device a respective illumination mask that identifies pixels that represent a portion of the environment that is exposed to high illumination. Overlapping portions of those illumination masks may then be combined to produce a unified illumination map of an area of the environment. The unified illumination map identifies, for different portions of the environment, a probability that the portion is actually exposed to high illumination.

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: Described is a method for processing image data to determine if a portion of the imaged environment is exposed to high illumination, such as sunlight. In some implementations, image data from multiple different imaging devices may be processed to produce for each imaging device a respective illumination mask that identifies pixels that represent a portion of the environment that is exposed to high illumination. Overlapping portions of those illumination masks may then be combined to produce a unified illumination map of an area of the environment. The unified illumination map identifies, for different portions of the environment, a probability that the portion is actually exposed to high illumination.

Abstract: Described is a method for processing image data to determine if a portion of the imaged environment is exposed to high illumination, such as sunlight. In some implementations, image data from multiple different imaging devices may be processed to produce for each imaging device a respective illumination mask that identifies pixels that represent a portion of the environment that is exposed to high illumination. Overlapping portions of those illumination masks may then be combined to produce a unified illumination map of an area of the environment. The unified illumination map identifies, for different portions of the environment, a probability that the portion is actually exposed to high illumination.

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 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.