Neural Network for Drug-Induced Facial Change Generation
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Solution Overview
Problem
Current face image generating technologies using artificial intelligence do not effectively depict the changes in a person's face due to drug use, which is crucial for awareness and identification purposes.
Innovation Solution
A deep learning-based face generation algorithm is employed to create a face image that reflects changes caused by drug use, utilizing a neural network trained with facial feature information from drug criminals, which is gradually modified based on drug dosage or usage duration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional face image generating technology is used, then high resolution real face images can be generated, but the technology cannot effectively depict facial changes caused by drug use
Solution Approach 1:
The patent transforms the face image generation task from creating realistic appearances to manipulating facial feature parameters that reflect drug use effects. The system extracts facial feature information and generates multiple versions with gradually changed parameters representing different drug dosage levels, enabling precise depiction of drug-induced facial changes while maintaining the underlying real face structure
Solution Approach 2:
The patent segments the face image into distinct facial feature information components that can be independently manipulated. By extracting and separately processing different facial features, the system can apply specific transformations to depict drug use effects on individual features while preserving the overall face structure, thereby achieving both realism and drug use indication
2Reliability
If face images of drug criminals are used for training, then the neural network can learn drug-induced facial changes, but ethical concerns and data availability issues arise
Solution Approach 1:
The patent creates synthetic training data by generating multiple versions of face images with controlled facial feature modifications representing drug use effects. Instead of relying on scarce real drug criminal images, the system copies and transforms available face images to create a comprehensive training dataset that captures the spectrum of drug-induced facial changes without requiring actual criminal records
Solution Approach 2:
The patent performs preliminary extraction and organization of facial feature information from available face images before training the neural network. By pre-processing and structuring the training data with labeled facial features and simulated drug use variations, the system prepares a ready-to-use training dataset that eliminates the need to search for and verify authentic drug criminal images during the training process
Data Source
AI summary
There is provided a method for training a neural network for generating a deformed face image from a face image preformed by an apparatus including a memory and a processor. The method comprises acquiring a face image for training; extracting facial feature information from the face image for training; and training the neural network to generate a deformed face image on the basis of the facial feature information, wherein the training includes generating multiple facial feature information that is gradually changed depending on the dosage or use duration of drugs, using the facial feature information as input data for training.