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 language | English |
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Title of host publication | Proceedings - 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 |
Editors | Ronen Basri, Cornelia Fermuller, Aleix Martinez, René Vidal |
Place of Publication | Piscataway NJ USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1915-1922 |
Number of pages | 8 |
ISBN (Electronic) | 9781479951178 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | IEEE Conference on Computer Vision and Pattern Recognition 2014 - Columbus, United States of America Duration: 23 Jun 2014 → 28 Jun 2014 http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6909096 (IEEE Conference Proceedings) |
Conference
Conference | IEEE Conference on Computer Vision and Pattern Recognition 2014 |
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Abbreviated title | CVPR 2014 |
Country | United States of America |
City | Columbus |
Period | 23/06/14 → 28/06/14 |
Internet address |
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Keywords
- Deep Learning
- Face Recognition
- Image Set Classification