Deep neural networks for mobile person recognition with audio-visual signals

Research output: Chapter in Book/Conference paperChapter

Abstract

This chapter starts with a general and brief introduction of biometrics and audiovisual person recognition using mobile phone data. It begins with a discussion of what constitutes a biometric recognition system, and it then details the steps followed when audio-visual signals are used as inputs. This is followed by a review of the existing speaker and face recognition systems which have been evaluated on a mobile biometric database. We then discuss the key motivations of using deep neural network (DNN) for person recognition. We finally introduce a Deep Boltzmann Machine (DBM)- DNN, in short DBM-DNN, based framework for person recognition. An overview of the sections and sub-sections of this chapter is shown in Figure 4.1.
Original languageEnglish
Title of host publicationMobile Biometrics
EditorsGuodong Guo, Harry Wechsler
Place of PublicationLondon
PublisherThe Institution of Engineering and Technology
Chapter4
Pages97-129
ISBN (Electronic)9781785610967
ISBN (Print)9781785610950
DOIs
Publication statusPublished - Sep 2017

Publication series

NameIET Book Series on Advances in Biometrics

Fingerprint

Biometrics
Face recognition
Mobile phones
Deep neural networks

Cite this

Alam, M., Bennamoun, M., Togneri, R., & Sohel, F. (2017). Deep neural networks for mobile person recognition with audio-visual signals. In G. Guo, & H. Wechsler (Eds.), Mobile Biometrics (pp. 97-129). (IET Book Series on Advances in Biometrics). London: The Institution of Engineering and Technology. https://doi.org/10.1049/PBSE003E_ch4
Alam, Mohammad ; Bennamoun, Mohammed ; Togneri, Roberto ; Sohel, Ferdous. / Deep neural networks for mobile person recognition with audio-visual signals. Mobile Biometrics. editor / Guodong Guo ; Harry Wechsler. London : The Institution of Engineering and Technology, 2017. pp. 97-129 (IET Book Series on Advances in Biometrics).
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title = "Deep neural networks for mobile person recognition with audio-visual signals",
abstract = "This chapter starts with a general and brief introduction of biometrics and audiovisual person recognition using mobile phone data. It begins with a discussion of what constitutes a biometric recognition system, and it then details the steps followed when audio-visual signals are used as inputs. This is followed by a review of the existing speaker and face recognition systems which have been evaluated on a mobile biometric database. We then discuss the key motivations of using deep neural network (DNN) for person recognition. We finally introduce a Deep Boltzmann Machine (DBM)- DNN, in short DBM-DNN, based framework for person recognition. An overview of the sections and sub-sections of this chapter is shown in Figure 4.1.",
author = "Mohammad Alam and Mohammed Bennamoun and Roberto Togneri and Ferdous Sohel",
year = "2017",
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language = "English",
isbn = "9781785610950",
series = "IET Book Series on Advances in Biometrics",
publisher = "The Institution of Engineering and Technology",
pages = "97--129",
editor = "Guodong Guo and Harry Wechsler",
booktitle = "Mobile Biometrics",
address = "United Kingdom",

}

Alam, M, Bennamoun, M, Togneri, R & Sohel, F 2017, Deep neural networks for mobile person recognition with audio-visual signals. in G Guo & H Wechsler (eds), Mobile Biometrics. IET Book Series on Advances in Biometrics, The Institution of Engineering and Technology, London, pp. 97-129. https://doi.org/10.1049/PBSE003E_ch4

Deep neural networks for mobile person recognition with audio-visual signals. / Alam, Mohammad; Bennamoun, Mohammed; Togneri, Roberto; Sohel, Ferdous.

Mobile Biometrics. ed. / Guodong Guo; Harry Wechsler. London : The Institution of Engineering and Technology, 2017. p. 97-129 (IET Book Series on Advances in Biometrics).

Research output: Chapter in Book/Conference paperChapter

TY - CHAP

T1 - Deep neural networks for mobile person recognition with audio-visual signals

AU - Alam, Mohammad

AU - Bennamoun, Mohammed

AU - Togneri, Roberto

AU - Sohel, Ferdous

PY - 2017/9

Y1 - 2017/9

N2 - This chapter starts with a general and brief introduction of biometrics and audiovisual person recognition using mobile phone data. It begins with a discussion of what constitutes a biometric recognition system, and it then details the steps followed when audio-visual signals are used as inputs. This is followed by a review of the existing speaker and face recognition systems which have been evaluated on a mobile biometric database. We then discuss the key motivations of using deep neural network (DNN) for person recognition. We finally introduce a Deep Boltzmann Machine (DBM)- DNN, in short DBM-DNN, based framework for person recognition. An overview of the sections and sub-sections of this chapter is shown in Figure 4.1.

AB - This chapter starts with a general and brief introduction of biometrics and audiovisual person recognition using mobile phone data. It begins with a discussion of what constitutes a biometric recognition system, and it then details the steps followed when audio-visual signals are used as inputs. This is followed by a review of the existing speaker and face recognition systems which have been evaluated on a mobile biometric database. We then discuss the key motivations of using deep neural network (DNN) for person recognition. We finally introduce a Deep Boltzmann Machine (DBM)- DNN, in short DBM-DNN, based framework for person recognition. An overview of the sections and sub-sections of this chapter is shown in Figure 4.1.

U2 - 10.1049/PBSE003E_ch4

DO - 10.1049/PBSE003E_ch4

M3 - Chapter

SN - 9781785610950

T3 - IET Book Series on Advances in Biometrics

SP - 97

EP - 129

BT - Mobile Biometrics

A2 - Guo, Guodong

A2 - Wechsler, Harry

PB - The Institution of Engineering and Technology

CY - London

ER -

Alam M, Bennamoun M, Togneri R, Sohel F. Deep neural networks for mobile person recognition with audio-visual signals. In Guo G, Wechsler H, editors, Mobile Biometrics. London: The Institution of Engineering and Technology. 2017. p. 97-129. (IET Book Series on Advances in Biometrics). https://doi.org/10.1049/PBSE003E_ch4