Learning non-linear reconstruction models for image set classification
© 2014 IEEE. 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 Boltzman...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/69852 |
| _version_ | 1848762150146801664 |
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| author | Hayat, M. Bennamoun, M. An, Senjian |
| author_facet | Hayat, M. Bennamoun, M. An, Senjian |
| author_sort | Hayat, M. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2014 IEEE. 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. |
| first_indexed | 2025-11-14T10:42:59Z |
| format | Conference Paper |
| id | curtin-20.500.11937-69852 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:42:59Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-698522018-08-08T04:56:39Z Learning non-linear reconstruction models for image set classification Hayat, M. Bennamoun, M. An, Senjian © 2014 IEEE. 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/69852 10.1109/CVPR.2014.246 restricted |
| spellingShingle | Hayat, M. Bennamoun, M. An, Senjian Learning non-linear reconstruction models for image set classification |
| title | Learning non-linear reconstruction models for image set classification |
| title_full | Learning non-linear reconstruction models for image set classification |
| title_fullStr | Learning non-linear reconstruction models for image set classification |
| title_full_unstemmed | Learning non-linear reconstruction models for image set classification |
| title_short | Learning non-linear reconstruction models for image set classification |
| title_sort | learning non-linear reconstruction models for image set classification |
| url | http://hdl.handle.net/20.500.11937/69852 |