Facial Similarity Matching for AI Blind Date Recommendations

The patent changes the matching parameter from condition-based attributes (region, age, occupation) to facial feature-based similarity. By extracting facial landmarks and calculating geometric relationships between landmarks, the system transforms the matching criterion into a more intuitive visual similarity metric that aligns with human instinctive attraction, thereby improving matching success rate while maintaining system adaptability

The patent replaces the mechanical condition-matching system with an AI-based facial recognition and similarity analysis system. Instead of manually comparing user-input conditions, the system uses deep learning models to automatically extract facial features and calculate similarity scores, enabling the system to adapt to human psychological preferences without requiring explicit user input about attraction criteria

The patent segments the user base into different categories or groups before performing detailed facial similarity analysis. By dividing the large dataset into smaller segments, the system can perform AI deep learning comparisons more efficiently within each segment, maintaining high facial similarity accuracy while reducing the overall time required to process all users

The patent performs preliminary filtering or pre-processing of user data before conducting comprehensive facial similarity analysis. This may include initial screening based on basic criteria or pre-extraction of key facial features, allowing the system to maintain high measurement precision while reducing the time loss associated with analyzing all users from scratch