Microsoft Technology Licensing, LLC

Chunkit Jacky Chan

David Benjamin Lee

Allison Light

Alexander Sabo Brodie

John Michael Thornton

Karthik G Anbalagan

Scott Robert Ramsby

Michael H Kim

Timothy R O'Connor

Bryan P Ferguson

Stacia Scott

Doug Ricard

Dahey Yoo

Daniel Rosenstein

David J Lee

Carmen Zlateff

Gyorgy K Schadt

Bradley G Weed

Ramona Canan

Omkar Anand Mehendale

Stacia L C Scott

Ivan D Romashka

Adam K Avery

Samuel J Pinson

Ian N Peth

Aanand Narayanan Pisharathu

Jae Pum Park

Benjamin N Truelove

William David Sproule

Eric W Richards

Eric Wayne Doerr

Ankur Khator

Shabbir A Shahpurwala

Ryan A Kabir

Shawn Lee Morrissey

William Mountain Lewis

Samuel James Pinson

Shanjeef Satchithanantham

Mindy C King

Jenny Linnéa Maria Ahlbäck

Jasmina S Mathieu

Aanand Narayanan Pulamanthole Pisharathu

Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Image scoring and identification based on facial feature descriptors

Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Creation of representative content based on facial analysis

Embodiments relate to leveraging automated image understanding to foster user engagement with an image. An image viewer application is provided with image analysis algorithms that determine features of an image by identifying objects in the image, recognizing faces in the image, etc. Hotspots may be added to the display of the image at locations of the features. Contextual metadata such as data about a user, user activity, other images, etc. can be used to determine which features will be provided with hotspots and what information will be associated with the hotspots. In one embodiment, the information associated with a hotspot is another image, and interactive selection of the hotspot causes the image to be both displayed and processed for hotspot augmentation. Tools may be provided to modify or edit the image based on the features found via the image analysis.

Using image understanding to assist user engagement

Grouping and ranking images based on facial recognition data

Media files may be prioritized based on user behavior data and social data associated with individual media files. A process of prioritizing media files may include accessing user behavior data associated with individual media files in a set of media files that are stored on a client device of a user or in a cloud-based storage location associated with the user, accessing social data associated with one or more media files of the set of media files that are determined to have been shared on a media file sharing service, and ranking the set of media files based at least in part on the user behavior data and the social data.

Prioritizing media based on social data and user behavior

Image curation techniques are described. In one or more implementations, one or more modules are launched for execution on a computing device. Responsive to the launch, criteria are generated automatically and without user intervention through execution of the one or more modules by a computing device. Images are curated using the generated criteria automatically and without user intervention through execution of the one or more modules, the curated images identified from a plurality of different image sources that are associated with a user. The curated images are caused to be displayed by a display device of the computing device automatically and without user intervention through execution of the one or more modules.

Image curation

An image to be shared with other users based on input from a first user is received. A second user is identified from a tag of the image, and information is provided, based at least in part on the tag, to one or both of the first user and the second user. Additionally, after editing of an image a determination can be made as to whether a region of the image having an associated tag has been affected by the editing. The tag associated with the region is altered if the region has been affected by the editing, otherwise the tag associated with the region is left unaltered. Furthermore, the tag can include a first portion storing data identifying a region of the image to which the tag corresponds, and a second portion storing data identifying a person shown in the region.

Tagging images with labels

A global tag for an image is received identifies one or more objects in the image, and a region-specific tag for the image identifies one or more objects in a region of the image. The global tag and the region-specific tag are stored with the image. Displayed, along with the image, is an identifier for each of the one or more objects identified in the global tag, and an identifier for each of the one or more objects identified in the region-specific tag. Different users are able to maintain different names for the same person, allowing the same tag of the image to be used as the basis for displaying the image with different names for the different users. Additionally, the tags can be used as a basis for generating a credits list of people that are included in a compilation of images.

Image tagging user interface

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Redmond, WA, United States of America

Chunkit Jacky Chan