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• W2068163954 abstract "We propose a deep learning framework for image set classification with application to face recognition. An Adaptive Deep Network Template (ADNT) is defined whose parameters are initialized by performing unsupervised pre-training in a layer-wise fashion using Gaussian Restricted Boltzmann Machines (GRBMs). The pre-initialized ADNT is then separately trained for images of each class and class-specific models are learnt. Based on the minimum reconstruction error from the learnt class-specific models, a majority voting strategy is used for classification. The proposed framework is extensively evaluated for the task of image set classification based face recognition on Honda/UCSD, CMU Mobo, YouTube Celebrities and a Kinect dataset. Our experimental results and comparisons with existing state-of-the-art methods show that the proposed method consistently achieves the best performance on all these datasets." @default.
• W2068163954 created "2016-06-24" @default.
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• W2068163954 date "2014-06-01" @default.
• W2068163954 modified "2023-10-18" @default.
• W2068163954 title "Learning Non-linear Reconstruction Models for Image Set Classification" @default.
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• W2068163954 doi "https://doi.org/10.1109/cvpr.2014.246" @default.
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