Acquiring/Recognising Facial Features TRIZ Cases
- Auto Pre-Capture Control for Dynamic Subject Imaging
Subject identification triggers automatic pre-capture, retaining buffered images to eliminate lag while reducing power consumption.
- Automated Facial Image Analysis for Syndrome Diagnosis
An automated image analysis system detects anatomical landmarks and extracts geometric features to classify facial conditions.
- Selective Interactive Event Tracking via Facial Expression Analysis
Facial expression classifier detects user interest to selectively record and tag interactive event data, reducing volume while preserving analytical value.
- Neural Network Delirium Prediction via Feature Point Tracking
A neural network learns delirium probability from moving image feature points.
- Multi-Domain Adversarial Learning for Facial Expression Recognition
Multi-domain adversarial learning segments datasets into specialized sub-domains, enabling high recognition accuracy with minimal training samples.
- Real-time Lip Movement Capture via Pretrained Average Model
Pretraining a lip average model offline separates heavy training from inference, enabling real-time capture without extensive sample collection delays.