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Mahalanobis Distance for Face Recognition
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If two vectors originate from the same underlying distribution, the distance
between them could be computed with the Mahalanobis distance, a generalization of the
Euclidean one. Also, it can be defined as the Euclidean distance computed in the
Mahalanobis space. Moreover, there exist also the city block-based Mahalanobis
distance and other versions including the angle- and cosine-based ones. Largely
employed for face recognition with bi-dimensional facial data, Mahalanobis gains very
good performances with PCA algorithms.
Title: Mahalanobis Distance for Face Recognition
Description:
If two vectors originate from the same underlying distribution, the distance
between them could be computed with the Mahalanobis distance, a generalization of the
Euclidean one.
Also, it can be defined as the Euclidean distance computed in the
Mahalanobis space.
Moreover, there exist also the city block-based Mahalanobis
distance and other versions including the angle- and cosine-based ones.
Largely
employed for face recognition with bi-dimensional facial data, Mahalanobis gains very
good performances with PCA algorithms.
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