A deep neural network for audio-visual person recognition

M.R. Alam; Mohammed Bennamoun; Roberto Togneri; Ferdous Sohel

doi:10.1109/BTAS.2015.7358754

A deep neural network for audio-visual person recognition

M.R. Alam, Mohammed Bennamoun, Roberto Togneri, Ferdous Sohel

Oceans Graduate School

Research output: Chapter in Book/Conference paper › Conference paper

6 Citations (Scopus)

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
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	https://doi.org/10.1109/BTAS.2015.7358754
Publication status	Published - 2015
Event	Biometrics Theory, Applications and Systems (BTAS) 2015 - Virginia, United States Duration: 8 Sep 2015 → 11 Sep 2015

Conference

Conference	Biometrics Theory, Applications and Systems (BTAS) 2015
Country	United States
City	Virginia
Period	8/09/15 → 11/09/15

Access to Document

10.1109/BTAS.2015.7358754

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Cite this

@inproceedings{d10bc81c82bf45ccb3c96b45a1457a73,

title = "A deep neural network for audio-visual person recognition",

abstract = "{\textcopyright} 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.",

author = "M.R. Alam and Mohammed Bennamoun and Roberto Togneri and Ferdous Sohel",

year = "2015",

doi = "10.1109/BTAS.2015.7358754",

language = "English",

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volume = "N/A",

pages = "1--6",

booktitle = "Biometrics Theory, Applications and Systems (BTAS), 2015 IEEE 7th International Conference",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "Biometrics Theory, Applications and Systems (BTAS) 2015 ; Conference date: 08-09-2015 Through 11-09-2015",

}

Alam, MR, Bennamoun, M , Togneri, R & Sohel, F 2015, A deep neural network for audio-visual person recognition. in Biometrics Theory, Applications and Systems (BTAS), 2015 IEEE 7th International Conference . vol. N/A, IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1-6, Biometrics Theory, Applications and Systems (BTAS) 2015, Virginia, United States, 8/09/15. https://doi.org/10.1109/BTAS.2015.7358754

A deep neural network for audio-visual person recognition. / Alam, M.R.; Bennamoun, Mohammed ; Togneri, Roberto ; Sohel, Ferdous.

Biometrics Theory, Applications and Systems (BTAS), 2015 IEEE 7th International Conference . Vol. N/A USA : IEEE, Institute of Electrical and Electronics Engineers, 2015. p. 1-6.

Research output: Chapter in Book/Conference paper › Conference paper

TY - GEN

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AU - Bennamoun, Mohammed

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AU - Sohel, Ferdous

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N2 - © 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.

AB - © 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.

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