Deep Learning Makeup Identification System
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Solution Overview
Problem
Conventional content management systems fail to effectively detect and search for makeup in digital images, relying on text-based queries that do not accurately represent visual makeup features, leading to unsuitable search results for users.
Innovation Solution
The implementation of a makeup identification system using deep learning, specifically a trained discriminative neural network that identifies and describes makeup characteristics in digital images by analyzing differences between images with and without makeup, allowing for image searches based on these characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based search queries are used to search for makeup in digital images, then the system is simple to operate, but the search results are not accurate or suitable
Solution Approach 1:
The patent replaces text-based search mechanisms with a deep learning-based image analysis system. A trained neural network automatically detects and identifies makeup characteristics in digital images, substituting manual text query input with automated visual analysis. This resolves the contradiction by achieving accurate makeup detection through algorithmic image processing rather than simple text matching.
Solution Approach 2:
The patent introduces an intermediate processing layer between the user and the image database. The deep learning model acts as an intermediary that automatically extracts makeup features from images and generates relevant search queries or tags, bridging the gap between simple user input and accurate search results without requiring users to manually craft complex text queries.
2Difficulty of detecting and measuring
If conventional image search systems are used, then the system is easy to operate, but it cannot detect or identify makeup characteristics in images
Solution Approach 1:
The system performs self-service by automatically detecting and identifying makeup characteristics without requiring user intervention. The deep learning model autonomously analyzes images, identifies makeup presence and types, and generates search results based on detected features. This eliminates the need for users to manually tag or describe makeup in images, maintaining ease of operation while enabling advanced detection capabilities.
3Measurement precision
If deep learning is used to identify makeup characteristics, then detection accuracy is improved, but computational resources and training time are required
Solution Approach 1:
The patent applies preliminary action by pre-training the deep learning model on a comprehensive dataset of images with and without makeup. The neural network is trained in advance to recognize various makeup characteristics, allowing it to perform accurate real-time detection without requiring extensive computational resources during actual use. The heavy computational work is performed during the offline training phase rather than during live operations.
Data Source
AI summary
Makeup identification using deep learning in a digital medium environment is described. Initially, a user input is received to provide a digital image depicting a face which has a desired makeup characteristic. A discriminative neural network is trained to identify and describe makeup characteristics of the input digital image based on data describing differences in visual characteristics between pairs of images, which include a first image depicting a face with makeup applied and a second image depicting a face without makeup applied. The makeup characteristics identified by the discriminative neural network are displayed for selection to search for similar digital images that have the selected makeup characteristic. Once retrieved, the similar digital images can be displayed along with the input digital image having the desired makeup characteristic.