Fingerprint Pattern Classification Using Certainty Factors

The patent replaces traditional rule-based and manual fingerprint classification methods with a machine learning-based automatic classification system. The learning model automatically extracts features from fingerprint images and classifies pattern types, substituting mechanical/manual processes with intelligent algorithms that achieve higher accuracy while maintaining manageable system complexity through modular architecture.

The patent transforms the classification approach by changing from fixed rule-based parameters to dynamic learning-based parameters. The system uses multiple learning models that can adapt their parameters based on training data, enabling more accurate pattern type classification by learning optimal decision boundaries from examples rather than relying on predetermined rules.

The patent implements preliminary action by using the certainty factor to pre-assess the reliability of fingerprint pattern type classification before proceeding to full verification. This allows the system to quickly filter out low-confidence cases that may require additional processing or manual review, while high-confidence cases can proceed directly to verification, reducing overall processing time while maintaining high reliability.

The patent incorporates feedback mechanisms where the certainty factor from the learning model provides information about classification confidence. This feedback allows the system to adjust its verification process dynamically - high certainty results can be accepted with minimal additional verification, while low certainty results trigger re-examination or alternative verification methods, optimizing both speed and reliability.