Biometric Identification Using Periodic Feature Extraction

The patent segments the biometric identification process into distinct modular components: signal acquisition module that collects multiple biometric modalities, feature extraction module that processes each modality separately to extract relevant characteristics, and matching module that compares extracted features against stored profiles. This segmentation allows the system to handle complex multi-modality data through organized, manageable stages, improving reliability while controlling complexity through structured processing.

The patent implements a universal biometric identification system that can process multiple different biometric modalities (such as physiological and behavioral signals) through a single integrated framework. The system uses a common feature extraction and matching architecture that adapts to different signal types, allowing one system to perform multiple identification functions. This multi-functionality approach improves reliability by combining multiple modalities while avoiding the complexity of separate dedicated systems for each modality.

The patent applies preliminary action by pre-processing biometric signals during acquisition phases and pre-extracting features before actual identification is needed. The system performs initial signal conditioning, filtering, and feature detection in advance, so that when identification is required, the computationally intensive matching operations can be executed quickly against pre-prepared reference profiles. This preliminary processing reduces the time burden during critical real-time identification moments.

The patent extracts and isolates only the most critical and discriminative features from complete biometric signals for the matching process. Rather than processing entire raw signals during identification, the system extracts key periodic features, transient characteristics, and distinctive patterns that are sufficient for accurate matching. This extraction approach reduces processing time by focusing computational resources on essential identification elements while maintaining accuracy.