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DLDA and LDA/QR Equivalence Framework for Human Face Recognition

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Executive Summary

Singularity problem in human face feature extraction is very challenging that has gained a lot of attentions during the last decade. A pseudo-inverse Linear Discriminant Analysis (LDA) plays an important role to solve the singularity problem of the scatter matrices. In this paper, the authors make use of Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant Analysis (DLDA) to solve the singularity problem in face feature recognition. They also show that an equivalent relationship between DLDA and LDA/QR. They can be regarded as a special case of pseudo-inverse LDA. Similar to LDA/QR algorithm, DLDA could be a two-stage LDA method. Interestingly, they find that the first stage of DLDA can act as a dimensionality reduction algorithm.

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