Invention Application
- Patent Title: UTILIZING A MACHINE LEARNING MODEL TRAINED TO DETERMINE SUBTLE POSE DIFFERENTIATIONS TO AUTOMATICALLY CAPTURE DIGITAL IMAGES
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Application No.: US17075207Application Date: 2020-10-20
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Publication No.: US20220121841A1Publication Date: 2022-04-21
- Inventor: Jinoh Oh , Xin Lu , Gahye Park , Jen-Chan Jeff Chien , Yumin Jia
- Applicant: Adobe Inc.
- Applicant Address: US CA San Jose
- Assignee: Adobe Inc.
- Current Assignee: Adobe Inc.
- Current Assignee Address: US CA San Jose
- Main IPC: G06K9/00
- IPC: G06K9/00 ; G06K9/62 ; G06T7/00

Abstract:
The present disclosure describes systems, non-transitory computer-readable media, and methods for utilizing a machine learning model trained to determine subtle pose differentiations to analyze a repository of captured digital images of a particular user to automatically capture digital images portraying the user. For example, the disclosed systems can utilize a convolutional neural network to determine a pose/facial expression similarity metric between a sample digital image from a camera viewfinder stream of a client device and one or more previously captured digital images portraying the user. The disclosed systems can determine that the similarity metric satisfies a similarity threshold, and automatically capture a digital image utilizing a camera device of the client device. Thus, the disclosed systems can automatically and efficiently capture digital images, such as selfies, that accurately match previous digital images portraying a variety of unique facial expressions specific to individual users.
Public/Granted literature
Information query