False Eyelash Selection Through Facial Feature Image Analysis

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Solution Overview

Problem

Conventional systems struggle to identify false eyelashes that are tailored to a user's unique facial features and geometry, leading to overwhelming choices and difficulties in application.

Innovation Solution

Utilizing image processing and machine learning techniques to generate textual identifiers describing facial features, which are used to create prompts for generative models to select and recommend suitable false eyelashes based on individual facial characteristics.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If conventional systems provide multiple false eyelash options, then user choice increases, but user decision difficulty and application complexity increase

Engineering Contradiction:

Improvefalse eyelash selection adaptabilityVSAvoiduser operation ease

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system performs automatic facial feature detection, analysis, and matching without requiring manual user input. The machine learning model autonomously processes facial images, identifies key features, and recommends suitable false eyelashes, eliminating the need for users to manually evaluate multiple options and make complex decisions.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual selection processes with automated machine learning-based image processing. Instead of users physically examining and comparing false eyelash products, the system uses computer vision and AI algorithms to analyze facial features and automatically generate personalized recommendations.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Device complexity

If generic false eyelash recommendations are provided, then system simplicity is maintained, but facial feature matching precision deteriorates

Engineering Contradiction:

Improvesystem complexityVSAvoidfacial feature matching precision

Core Design Contradiction:

Device complexityVSMeasurement precision

Solution Approach 1:

The system analyzes multiple facial feature parameters including eye shape, size, spacing, and geometric relationships between facial features. By considering these detailed parameters and their interrelationships, the system achieves precise matching without requiring overly complex system architecture.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The machine learning model is pre-trained on extensive facial feature data and false eyelash specifications before deployment. This preliminary training enables the system to perform accurate matching operations using established algorithms, balancing computational efficiency with high precision in facial feature analysis and false eyelash recommendation.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20250322690A1Using image proccessing, machine learning and images of a human face for prompt generation related to false eyelashes

Publication Date: 2025.10.16 LASHIFY INC

AI summary

A method includes determining a textual identifier that describes at least one facial feature of a human face based on two-dimensional image data representing the human face. The method further includes generating a prompt for a generative machine learning model. The prompt includes information corresponding to the textual identifier that describes the at least one facial feature of the human face. The method further includes obtaining, from the generative machine learning model and based on the prompt, an output indicative of a set of false eyelashes that suit the human face.