![]() Recognizing people in libraries consists of two interwoven phases. ![]() We want everyone to have the same extraordinary experience that we designed into the feature, no matter the photographic subject’s skin color, age, or gender. People all around the world use Apple products. This is especially true in photography of dynamic scenes, such as capturing a toddler bursting a bubble, or friends raising a glass for a toast.Īnother challenge, and a fundamental requirement for automatic person recognition, is to ensure equity in the results. When someone wants to view all their photos of a specific person, a comprehensive knowledge graph is needed, including instances where the subject is not posing for the image. People can appear at arbitrary scales, lighting, pose, and expression, and the images can be captured from any camera. The task of recognizing people in user-generated content is inherently challenging because of the sheer variability in the domain. D) Memories pane showing person-specific memories. Figure 1: A) Picture showing the identified people at the bottom left. Memories uses popular themes based on important people in a user’s life, such as a memory for “Together,” as shown in Figure 1D. The knowledge graph powers the beloved Memories feature in Photos, which creates engaging video vignettes centered around different themes in a user’s library. Photos can also learn from identity information to build a private, on-device knowledge graph that identifies interesting patterns in a user’s library, such as important groups of people, frequent places, past trips, events, the last time a user took an image of a certain person, and more. ![]() A user can then manually add names to people in their photos and find someone by typing the person’s name in the search bar, as shown in Figure 1C. A user can also directly access the People Album, shown in Figure 1B, to browse images and confirm the correct person is tagged in their images. As shown in Figure 1A, a user can scroll up on an image, tap on the circle representing the person that has been recognized in that image, and then pivot to browse their library to see images containing that person. Photos relies on identity information in a number of ways. An algorithm foundational to this goal recognizes people from their visual appearance. Photos uses a number of machine learning algorithms, running privately on-device, to help curate and organize images, Live Photos, and videos. Photos (on iOS, iPadOS, and macOS) is an integral way for people to browse, search, and relive life's moments with their friends and family.
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