Abstract: A system of injecting a code section to a code edited by a graphical user interface (GUI) of an integrated development environment (IDE), comprising: a memory storing a dataset associating each code segment with one hand pose feature or hand motion feature; an imager adapted to capture images of a hand while an IDE being executed on a client terminal; and processor for executing code of an application, comprising: code instructions to identify at least one of the features and at least one discrete value of the identified features from an analysis of the images; code instructions to select at least one of the code segments associated with the identified features; and code instructions to add automatically a code section generated based on the code segments and the discrete value to a code presented by a code editor of the IDE.

Abstract: A system for associating between a computerized model of multimodal human interaction and application functions, comprising: (a) An interface for receiving instructions from a programmer defining one or more application functions. (b) A memory storing hand gestures each defined by a dataset of discrete pose values and discrete motion values. (c) A code store storing a code. (d) One or more processors coupled to the interface, the memory and the code store for executing the stored code which comprises: (1) Code instructions to define a logical sequence of user input per instructions of the programmer. The logical sequence combines hand gestures with non-gesture user input. (2) Code instructions to associate the logical sequence with the application function(s) for initiating an execution of the application function(s) during runtime of the application in response to detection of the logical sequence by analyzing a captured data depicting a user during runtime.

Abstract: A system of inverse reconstruction of a skeleton model of a hand, comprising: an imager adapted to capture at least one image of a hand; a memory storing a plurality of hand pose features records, each defined by a unique set of discrete pose values; a code store storing a code; at least one processor coupled to the imager, memory and program store for executing the stored code, the code comprising: code instructions to identify a group of discrete pose values from an analysis of the at least one image; code instructions to select a hand pose features record from the hand pose features records according to the group of discrete pose values; and code instructions to reconstruct a skeleton model of the hand in the hand pose from the hand pose features record based on a hand model which maps kinematic characteristics of a plurality of hand organs.

Abstract: Fast general multipath correction in time of flight imaging is described, for example, to obtain accurate depth maps at frame rate from a time of flight camera. In various embodiments accurate depth maps are calculated by looking up corrected depth values stored in a look up table. In various embodiments the corrected depth values are highly accurate as they take into account three or more possible light ray paths between the camera and a surface in a scene being imaged. In an example accurate depth maps are computed at a frame rate of a time of flight camera. In an example accurate depth maps are computed in less than 30 milliseconds for an image having over 200,000 pixels using a standard CPU.

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 computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

Abstract: A computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

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: Fast general multipath correction in time of flight imaging is described, for example, to obtain accurate depth maps at frame rate from a time of flight camera. In various embodiments accurate depth maps are calculated by looking up corrected depth values stored in a look up table. In various embodiments the corrected depth values are highly accurate as they take into account three or more possible light ray paths between the camera and a surface in a scene being imaged. In an example accurate depth maps are computed at a frame rate of a time of flight camera. In an example accurate depth maps are computed in less than 30 milliseconds for an image having over 200,000 pixels using a standard CPU.

Abstract: Pose and gesture detection and classification of a human poses and gestures using a discriminative ferns ensemble classifier is provided. Sample image data in one or more channels includes a human image. A processing device operates on the sample image data using the discriminative ferns ensemble classifier. The classifier has set of classification tables and matching bit features (ferns) which are developed using a first set of training data and optimized by a weighting of the tables using an SVM linear classifier configured based on the first or a second set of pose training data. The tables allow computation of a score per pose class for the image in the sample data and the processor outputs a determination of the pose in the sample depth image data. The determination enables the manipulation of a natural user interface.

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: Depth map correction using lookup tables is described. In an example depth maps may be generated that measure a depth to an object using differences in phase between light transmitted from a camera which illuminates the object and light received at the camera which has been reflected from the object. In various embodiments depth maps may be subject to errors caused by received light undergoing multiple reflections before being received by the camera. In an example a correction for an estimated depth of an object may be computed and stored in a lookup table which maps the amplitude and phase of the received light to a depth correction. In an example the amplitudes and frequencies of each modulation frequency may be to access lookup table which stores corrections for the depth of an object and which allows an accurate depth map to be obtained.

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: In one embodiment, the present invention includes a method for training a Support Vector Machine (SVM) on a subset of features (d?) of a feature set having (d) features of a plurality of training instances to obtain a weight per instance, approximating a quality for the d features of the feature set using the weight per instance, ranking the d features of the feature set based on the approximated quality, and selecting a subset (q) of the features of the feature set based on the ranked approximated quality. Other embodiments are described and claimed.

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 method comprises receiving a wireless signal at a channel estimator, and receiving the wireless signal at a channel length determination unit. Each of the channel estimator and the channel length determination unit independently receive the wireless signal.

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.