Abstract
© 2015 IEEE. This paper presents applications of special types of deep neural networks (DNNs) for audio-visual biometrics. A common example is the DBN-DNN that uses the generative weights of deep belief networks (DBNs) to initialize the feature detecting layers of deterministic feed forward DNNs. In this paper, we propose the DBM-DNN that uses the generative weights of deep Boltzmann machines (DBMs) for initialization of DNNs. Then, a softmax layer is added on top and the DNNs are trained discriminatively. Our experimental results show that lower error rates can be achieved using the DBM-DNN compared to the support vector machine (SVM), linear regression-based classifier (LRC) and the DBN-DNN. Experiments were carried out on two publicly available audio-visual datasets: the VidTIMIT and MOBIO.
Original language | English |
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Title of host publication | Biometrics Theory, Applications and Systems (BTAS), 2015 IEEE 7th International Conference |
Place of Publication | USA |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1-6 |
Volume | N/A |
ISBN (Print) | 9781479987764 |
DOIs | |
Publication status | Published - 2015 |
Event | Biometrics Theory, Applications and Systems (BTAS) 2015 - Virginia, United States Duration: 8 Sept 2015 → 11 Sept 2015 |
Conference
Conference | Biometrics Theory, Applications and Systems (BTAS) 2015 |
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Country/Territory | United States |
City | Virginia |
Period | 8/09/15 → 11/09/15 |