Stored Digital Makeup Enhancements for Recognized Faces
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing face-editing tools lack the ability to automatically apply different digital makeup enhancements on a face-by-face basis during image capturing or editing, requiring users to manually repeat steps for each face to customize enhancements.
Innovation Solution
A computing device is configured to store customized digital makeup enhancements for individual users, allowing automatic application of these enhancements to recognized faces in real-time or stored images, using facial recognition to identify and apply previously saved settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual face-editing tools are used to apply digital makeup enhancements, then customization for each face is possible, but users must manually repeat steps for each face which increases time consumption and reduces efficiency
Solution Approach 1:
The system performs preliminary facial recognition and grouping before the actual makeup application. Faces are automatically detected, grouped by similarity, and pre-configured for batch processing, eliminating the need for manual repetition across multiple faces
Solution Approach 2:
The system creates copies of makeup enhancement settings from one face and automatically applies them to other faces in the same group. This copying mechanism allows consistent customization across multiple faces without manual reconfiguration
2Extent of automation
If automatic facial recognition is implemented to identify faces in images, then batch processing becomes possible, but the device must store and process reference facial data which increases storage requirements
Solution Approach 1:
The system stores reference facial data in a structured manner, organizing it by user and face group. Only relevant facial features and characteristics are stored as reference data, optimizing storage by focusing on essential identification attributes rather than complete image storage
Solution Approach 2:
The system extracts only the necessary facial characteristics and reference data needed for recognition and grouping, separating this from complete image storage. This extraction approach minimizes storage requirements while maintaining automatic identification capability
Data Source
Figure 1
Figure 2
Figure 3A~3B
AI summary
An example method includes outputting, by a computing device for display, one or more digital images that include a face of a user, receiving, by the computing device and based on a facial recognition process, an indication of a match between facial data associated with the face of the user and reference facial data associated with a face of an enrolled user of the computing device, and retrieving, by the computing device, digital makeup enhancement data that is associated with the reference facial data. Hie example method further includes applying, by the computing device, the digital makeup enhancement data to the facial data, of tire face of the user to generate one or more modified digital images that indicate at least one corresponding digital makeup enhancement to the face of the user, and outputting, by the computing device for display, the one or more modified digital images.