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In this paper, we propose a new multiview discriminative learning (MDL) method for age-invariant face recognition, which is a challenging and important problem in many practical face recognition systems. Motivated by the fact that local appearance features are more robust to age variations, we first extract three different local feature descriptors including scale invariant feature transform (SIFT),...
We propose in this paper a multilinear locality preserving canonical correlation analysis (MLPCCA) method for face recognition. Motivated by the fact that both spatial structure information within each face sample and local geometry information among multiple face samples are useful for facial image feature extraction, we utilize them simultaneously and derive an improved canonical correlation analysis...
Age estimation is an important enabling capability for the near future, especially in applications related to Human Computer Interaction. Perhaps due to technical difficulties, age estimation has only recently begun to receive more attention. One of the more important pre-processing steps before age estimation is facial alignment, which spatially transforms a face image to align certain facial features,...
In this paper, a novel face representation in terms of dense local image descriptors is proposed. Scale Invariant Feature Transform (SIFT) and Gabor, two of the most popular local image descriptors, at dense grid pixels of a face image are used to represent the face. The efficiency of the representation has been investigated in gender recognition. There are four problems when applying the SIFT to...
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