A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

Alireza Sepas-Moghaddam, Mohammad Ahsanul Haque, Paulo Lobato Correia, Kamal Nasrollahi, Thomas B. Moeslund, Fernando Pereira

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions, computed using a VGG- 16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then be analysed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.
OriginalsprogEngelsk
TidsskriftI E E E Transactions on Circuits and Systems for Video Technology
ISSN1051-8215
StatusAccepteret/In press - 8 maj 2019

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Face recognition
Biometrics
Cameras
Neural networks
Experiments
Long short-term memory

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    @article{18da86205754469896b268df35174379,
    title = "A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition",
    abstract = "Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions, computed using a VGG- 16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then be analysed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.",
    keywords = "Face Recognition, Lenslet Light Field Imaging, Spatio-Angular Information, Deep Learning, VGG-Face Descriptor, VGG-Very-Deep-16 CNN, Long Short-Term Memory Network",
    author = "Alireza Sepas-Moghaddam and Haque, {Mohammad Ahsanul} and Correia, {Paulo Lobato} and Kamal Nasrollahi and Moeslund, {Thomas B.} and Fernando Pereira",
    year = "2019",
    month = "5",
    day = "8",
    language = "English",
    journal = "I E E E Transactions on Circuits and Systems for Video Technology",
    issn = "1051-8215",
    publisher = "IEEE",

    }

    A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition. / Sepas-Moghaddam, Alireza ; Haque, Mohammad Ahsanul; Correia, Paulo Lobato; Nasrollahi, Kamal; Moeslund, Thomas B.; Pereira, Fernando.

    I: I E E E Transactions on Circuits and Systems for Video Technology, 08.05.2019.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - A Double-Deep Spatio-Angular Learning Framework for Light Field based Face Recognition

    AU - Sepas-Moghaddam, Alireza

    AU - Haque, Mohammad Ahsanul

    AU - Correia, Paulo Lobato

    AU - Nasrollahi, Kamal

    AU - Moeslund, Thomas B.

    AU - Pereira, Fernando

    PY - 2019/5/8

    Y1 - 2019/5/8

    N2 - Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions, computed using a VGG- 16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then be analysed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.

    AB - Face recognition has attracted increasing attention due to its wide range of applications, but it is still challenging when facing large variations in the biometric data characteristics. Lenslet light field cameras have recently come into prominence to capture rich spatio-angular information, thus offering new possibilities for advanced biometric recognition systems. This paper proposes a double-deep spatio-angular learning framework for light field based face recognition, which is able to model both the intra-view/spatial and inter-view/angular information using two deep networks in sequence. This is a novel recognition framework that has never been proposed in the literature for face recognition or any other visual recognition task. The proposed double-deep learning framework includes a long short-term memory (LSTM) recurrent network whose inputs are VGG-Face descriptions, computed using a VGG- 16 convolutional neural network (CNN). The VGG-Face spatial descriptions are extracted from a selected set of 2D sub-aperture (SA) images rendered from the light field image, corresponding to different observation angles. A sequence of the VGG-Face spatial descriptions is then be analysed by the LSTM network. A comprehensive set of experiments has been conducted using the IST-EURECOM light field face database, addressing varied and challenging recognition tasks. Results show that the proposed framework achieves superior face recognition performance when compared to the state-of-the-art.

    KW - Face Recognition

    KW - Lenslet Light Field Imaging

    KW - Spatio-Angular Information

    KW - Deep Learning

    KW - VGG-Face Descriptor

    KW - VGG-Very-Deep-16 CNN

    KW - Long Short-Term Memory Network

    M3 - Journal article

    JO - I E E E Transactions on Circuits and Systems for Video Technology

    JF - I E E E Transactions on Circuits and Systems for Video Technology

    SN - 1051-8215

    ER -