Facial Attribute Model for Accurate Image Effect Addition
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Solution Overview
Problem
Existing image processing technologies struggle to accurately add facial effects to images, often resulting in distortions and poor effect addition, leading to a low reality of the effect images and a poor user experience.
Innovation Solution
An image processing method and apparatus that utilize a target facial attribute determination model to process data, including Gaussian noise or images, to generate a target facial image with optimally matched facial changes, ensuring high effect reality and improved user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional image retouching software is used to add effects to objects in images, then effects can be added to enhance video and photo content, but distortions of the limbs, face, and other body parts are likely to be caused, leading to poor effect addition
Solution Approach 1:
The patent replaces traditional mechanical image retouching operations with a deep learning-based attribute determination model. The model automatically identifies facial attributes (gender, age, expression, etc.) and applies appropriate effects, eliminating the need for manual retouching operations that cause distortions. The neural network processes images to generate target facial images with accurately matched effects without manual intervention.
Solution Approach 2:
The patent changes the processing parameters by using a trained attribute determination model with specific structural parameters (convolutional layers, fully connected layers, dropout rates) to transform input images into target facial images with modified attributes. The model adjusts parameters such as gender, age, and expression to apply effects that match the desired transformation while maintaining facial accuracy.
2Adaptability or versatility
If traditional image retouching methods are used, then effects can be applied to images, but the reality of the effect images is low and user experience is poor
Solution Approach 1:
The patent replaces manual retouching mechanics with an automated deep learning system that determines facial attributes and applies effects realistically. The attribute determination model processes images through multiple neural network layers to generate target facial images with effects that closely match real-world appearances, significantly improving effect reality and user experience.
Solution Approach 2:
The patent incorporates feedback mechanisms where the attribute determination model is trained using training images and their corresponding target images. The model learns from the feedback of prediction errors and adjusts its parameters to improve accuracy. During operation, the model continuously refines its attribute determination based on the input image characteristics to produce realistic effect images.
Data Source
AI summary
Provided are an image processing method and apparatus, an electronic device, and a storage medium. The image processing method comprises: obtaining data to be processed; and processing the data to be processed on the basis of a target facial attribute determination model to obtain a target facial image corresponding to the data to be processed, wherein at least one target feature in the target facial image matches with at least one corresponding preset facial feature.