Journal article
A joint Deep Boltzmann Machine (jDBM) model for person identification using mobile phone data
IEEE Transactions on Multimedia, Vol.19(2), pp.317-326
2016
Abstract
We propose an audio-visual person identification approach based on a joint deep Boltzmann machine (jDBM) model. The proposed jDBM model is trained in three steps: a) learning the unimodal DBM models corresponding to the speech and facial image modalities, b) learning the shared layer parameters using a joint Restricted Boltzmann Machine (jRBM) model and c) the fine-tuning of the jDBM model after the initialization with the parameters of the unimodal DBMs and the shared layer. The activation probabilities of the units of the shared layer are used as the joint features and a logistic regression classifier is used for the combined speech and facial image recognition. We show that by learning the shared layer parameters using a jRBM, a higher accuracy can be achieved compared to the greedy layer-wise initialization. The performance of our proposed model is also compared with state-of-the art support vector machine (SVM), deep belief network (DBN), and the deep auto-encoder (DAE) models. In addition, our experimental results show that the joint representations obtained from the proposed jDBM model are robust to noise and missing information. Experiments were carried out on the challenging MOBIO database, which includes audio-visual data captured using mobile phones.
Details
- Title
- A joint Deep Boltzmann Machine (jDBM) model for person identification using mobile phone data
- Authors/Creators
- M. Alam (Author/Creator)M. Bennamoun (Author/Creator)R. Togneri (Author/Creator)F. Sohel (Author/Creator)
- Publication Details
- IEEE Transactions on Multimedia, Vol.19(2), pp.317-326
- Publisher
- IEEE
- Identifiers
- 991005542038907891
- Copyright
- © IEEE 2017
- Murdoch Affiliation
- School of Engineering and Information Technology
- Language
- English
- Resource Type
- Journal article
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InCites Highlights
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- Collaboration types
- Domestic collaboration
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.17 Computer Vision & Graphics
- 4.17.118 Face Recognition
- Web Of Science research areas
- Computer Science, Information Systems
- Computer Science, Software Engineering
- Telecommunications
- ESI research areas
- Computer Science