Learning non-linear reconstruction models for image set classification

Munawar Hayat, Mohammed Bennamoun, Senjian An

Research output: A Conference proceeding or a Chapter in BookConference contribution

46 Citations (Scopus)
1 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationComputer vision and pattern recognition, 2014 IEEE Conference on
EditorsMunawar Hayat
Place of PublicationColumbus OH
PublisherIEEE
Pages1-8
Number of pages8
Volume1
ISBN (Electronic)9781479951185
DOIs
Publication statusPublished - 23 Jun 2014
Externally publishedYes
Event2014 IEEE Conference on Computer Vision and Pattern Recognition - Columbus, Columbus, United States
Duration: 23 Jun 201428 Jun 2014

Conference

Conference2014 IEEE Conference on Computer Vision and Pattern Recognition
Abbreviated titleCVPR 2014
CountryUnited States
CityColumbus
Period23/06/1428/06/14

Fingerprint

Face recognition
Deep learning

Cite this

Hayat, M., Bennamoun, M., & An, S. (2014). Learning non-linear reconstruction models for image set classification. In M. Hayat (Ed.), Computer vision and pattern recognition, 2014 IEEE Conference on (Vol. 1, pp. 1-8). Columbus OH: IEEE. https://doi.org/10.1109/cvpr.2014.246
Hayat, Munawar ; Bennamoun, Mohammed ; An, Senjian. / Learning non-linear reconstruction models for image set classification. Computer vision and pattern recognition, 2014 IEEE Conference on. editor / Munawar Hayat. Vol. 1 Columbus OH : IEEE, 2014. pp. 1-8
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title = "Learning non-linear reconstruction models for image set classification",
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.",
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Hayat, M, Bennamoun, M & An, S 2014, Learning non-linear reconstruction models for image set classification. in M Hayat (ed.), Computer vision and pattern recognition, 2014 IEEE Conference on. vol. 1, IEEE, Columbus OH, pp. 1-8, 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, United States, 23/06/14. https://doi.org/10.1109/cvpr.2014.246

Learning non-linear reconstruction models for image set classification. / Hayat, Munawar; Bennamoun, Mohammed; An, Senjian.

Computer vision and pattern recognition, 2014 IEEE Conference on. ed. / Munawar Hayat. Vol. 1 Columbus OH : IEEE, 2014. p. 1-8.

Research output: A Conference proceeding or a Chapter in BookConference contribution

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N2 - 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.

AB - 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.

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Hayat M, Bennamoun M, An S. Learning non-linear reconstruction models for image set classification. In Hayat M, editor, Computer vision and pattern recognition, 2014 IEEE Conference on. Vol. 1. Columbus OH: IEEE. 2014. p. 1-8 https://doi.org/10.1109/cvpr.2014.246