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Face annotation of photos, a key enabling technology for many exciting new applications, has been gaining broad interest. The task is different from the general face recognition problem because the dataset is not constrained - an unlabelled face may not have any corresponding match in the training set. Moreover, faces in real-life photos have a significantly wider variation range than those in the...
The Fisher Linear Discriminant (FLD) is commonly used in classification to find a subspace that maximally separates class patterns according to the Fisher Criterion. It was previously proven that a pre-whitening step can be used to truly optimize the Fisher Criterion. In this paper, we study the theoretical properties of the subspaces induced by this whitened FLD. Of the four subspaces induced, two...
We present Multimodal Discriminant Analysis (MMDA), a novel method for decomposing variations in a dataset into independent factors (modes). For face images, MMDA effectively separates personal identity, illumination and pose into orthogonal subspaces. MMDA is based on maximizing the Fisher Criterion on all modes at the same time, and is therefore well-suited for multimodal and mode-invariant pattern...
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