Face Sample Database Recognition

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Resource Overview

The system first utilizes "gettrainingdata" to extract facial features from the sample database where each individual has only one sample. Then "facerecognition" processes input faces for identification, delivering relatively accurate recognition results through feature matching algorithms.

Detailed Documentation

In this process, we initially employ the "gettrainingdata" function to extract facial features from the sample database. This database contains only one sample per individual, making it a single-sample-per-person recognition system. The algorithm likely implements feature extraction techniques such as PCA (Principal Component Analysis) or LDA (Linear Discriminant Analysis) to reduce dimensionality while preserving discriminative facial characteristics. Subsequently, the "facerecognition" module processes input facial images by comparing their feature vectors against the stored database using similarity measurement methods (e.g., Euclidean distance or cosine similarity). This approach enables relatively accurate face recognition by matching input features with pre-stored templates, achieving precise identification results through optimized pattern matching algorithms.

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