Fingerprint Medium Classification Using Feature Matrix Detection

The patent segments the fingerprint detection process into two distinct functional modules: one for authenticity verification (using acoustic impedance and surface distance) and another for medium type classification (using the trained classification network). This segmentation allows each module to specialize in its specific task, with the medium classification network focusing exclusively on identifying medium types without being constrained by the authenticity verification requirements.

The trained medium classification network serves multiple purposes: it classifies medium types (gel, latex, silicone, etc.), provides probability distributions for different medium types, and can work in conjunction with the authenticity verification system. This multi-functionality allows the system to simultaneously determine both the authenticity of the fingerprint and the type of medium, resolving the information loss about medium types.

The system performs preliminary classification of medium types before conducting detailed fingerprint analysis. By first identifying what type of medium the fingerprint is on (gel, latex, silicone, etc.), the system can then adapt its analysis parameters and methods accordingly, enabling more versatile and accurate fingerprint identification across different medium types.

The patent implements a dynamic analysis approach where the fingerprint identification process adapts based on the detected medium type. The system adjusts its analysis parameters, feature extraction methods, and matching criteria according to the specific medium characteristics, thereby enhancing the versatility and adaptability of fingerprint analysis while maintaining ease of operation through automated adaptation.

The trained medium classification network acts as an intermediary component between the fingerprint data acquisition system and the final analysis system. It receives raw fingerprint data, processes it through the classification network to determine medium type, and outputs this information to guide subsequent analysis. This intermediary approach isolates the complexity within a dedicated module while keeping the overall system architecture clean and manageable.

The patent uses a pre-trained classification network that can be independently developed, tested, and optimized separately from the main fingerprint analysis system. This copied module can be trained on extensive datasets and refined for high accuracy, then integrated into the broader system without requiring redesign of the entire system architecture, thereby managing complexity while maintaining high detection accuracy.