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This algorithm is used in various applications, such as feature extraction and matching.
The method they used was the SIFT key points extraction and matching.
This voting-like approach avoids establishing correspondences through computationally demanding feature extraction and matching processes.
These operations involve four main steps namely, image acquisition, iris segmentation, normalization, feature extraction, and matching [7].
As this quality degradation significantly influences the performance of feature extraction and matching processes, it must be handled properly.
From this point, different branches perform detection, segmentation, feature extraction, and matching for face and iris separately.
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The average time spent on key-point extraction, description, and matching, normalized by the total number of key-points processed are compared.
The unmatched reference data are calculated as false negative (FN). Figure 3 Matched extraction (a) and matched reference (b).
Unlike conventional methods doing feature extraction and feature matching separately, we pair patches from sensed and reference images, and then learn the mapping directly between these patch-pairs and their matching labels for later registration.
The proposed method consisted of three steps: blood vessel segmentation, feature extraction, and feature matching.
A typical person re-identification pipeline consists of two components: feature extraction and image matching.
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