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• W2292056273 abstract "Aging face recognition refers to matching the same person's faces across different ages, e.g., matching a person's older face to his (or her) younger one, which has many important practical applications, such as finding missing children. The major challenge of this task is that facial appearance is subject to significant change during the aging process. In this paper, we propose to solve the problem with a hierarchical model based on two-level learning. At the first level, effective features are learned from low-level microstructures, based on our new feature descriptor called local pattern selection (LPS). The proposed LPS descriptor greedily selects low-level discriminant patterns in a way, such that intra-user dissimilarity is minimized. At the second level, higher level visual information is further refined based on the output from the first level. To evaluate the performance of our new method, we conduct extensive experiments on the MORPH data set (the largest face aging data set available in the public domain), which show a significant improvement in accuracy over the state-of-the-art methods." @default.
• W2292056273 created "2016-06-24" @default.
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• W2292056273 date "2016-05-01" @default.
• W2292056273 modified "2023-10-14" @default.
• W2292056273 title "Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection" @default.
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• W2292056273 doi "https://doi.org/10.1109/tip.2016.2535284" @default.
• W2292056273 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26930681" @default.
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