Comparison of super-resolution benefits for downsampled iages and real low-resolution data

Yuxi Peng, Luuk Spreeuwers, Bert Gökberk, Raymond Veldhuis

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

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    Abstract

    Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy.
    Original languageEnglish
    Title of host publication34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013
    Subtitle of host publicationLeuven, Belgium, 30-31 May 2013
    PublisherWerkgemeenschap voor Informatie- en Communicatietheorie (WIC)
    Pages244-251
    Number of pages8
    ISBN (Print)9781627487375, 978-90-365-0000-5
    Publication statusPublished - 30 May 2013
    Event34th WIC Symposium on Information Theory in the Benelux 2013 - Leuven, Belgium
    Duration: 30 May 201331 May 2013
    Conference number: 34

    Conference

    Conference34th WIC Symposium on Information Theory in the Benelux 2013
    CountryBelgium
    CityLeuven
    Period30/05/1331/05/13

    Fingerprint

    Face recognition
    Optical resolving power
    Experiments
    Discriminant analysis
    Image resolution
    Principal component analysis
    Cameras
    Detectors

    Keywords

    • EWI-23950
    • SCS-Safety
    • Super-resolution
    • IR-87825
    • Face Recognition
    • METIS-300149
    • Performance Evaluation

    Cite this

    Peng, Y., Spreeuwers, L., Gökberk, B., & Veldhuis, R. (2013). Comparison of super-resolution benefits for downsampled iages and real low-resolution data. In 34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013: Leuven, Belgium, 30-31 May 2013 (pp. 244-251). Werkgemeenschap voor Informatie- en Communicatietheorie (WIC).
    Peng, Yuxi ; Spreeuwers, Luuk ; Gökberk, Bert ; Veldhuis, Raymond. / Comparison of super-resolution benefits for downsampled iages and real low-resolution data. 34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013: Leuven, Belgium, 30-31 May 2013. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2013. pp. 244-251
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    abstract = "Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy.",
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    Peng, Y, Spreeuwers, L, Gökberk, B & Veldhuis, R 2013, Comparison of super-resolution benefits for downsampled iages and real low-resolution data. in 34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013: Leuven, Belgium, 30-31 May 2013. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), pp. 244-251, 34th WIC Symposium on Information Theory in the Benelux 2013, Leuven, Belgium, 30/05/13.

    Comparison of super-resolution benefits for downsampled iages and real low-resolution data. / Peng, Yuxi; Spreeuwers, Luuk; Gökberk, Bert; Veldhuis, Raymond.

    34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013: Leuven, Belgium, 30-31 May 2013. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC), 2013. p. 244-251.

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

    TY - GEN

    T1 - Comparison of super-resolution benefits for downsampled iages and real low-resolution data

    AU - Peng, Yuxi

    AU - Spreeuwers, Luuk

    AU - Gökberk, Bert

    AU - Veldhuis, Raymond

    PY - 2013/5/30

    Y1 - 2013/5/30

    N2 - Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy.

    AB - Recently, more and more researchers are exploring the benefits of super-resolution methods on low-resolution face recognition. However, often results presented are obtained on downsampled high-resolution face images. Because downsampled images are different from real images taken at low resolution, it is important to include real surveillance data. In this paper, we investigate the difference between downsampled images and real surveillance data in two aspects: (1) the influence of resolution on face recognition accuracy, and (2) the improvement of accuracy that can be achieved by super-resolution on these images. Specifically, we will test the following hypotheses: (1) face recognition performance on real images is much worse than on downsampled images, and (2) super-resolution improves the performance of downsampled images more than real images. Our experiments are conducted using videos from the HumanID database. In each video, the target person’s face is captured while he is walking towards the surveillance camera. We detect the faces in the video frames using a Viola-Jones face detector. Then we select face images of four different resolutions: two low-resolution and two high-resolution. The high-resolution images are used for gallery and generating downsampled images. We perform two types of face recognition experiments. In the first type of experiments, three face recognition methods are evaluated for images with different resolution. The three methods are (1) Principal Component Analysis, (2) Linear Discriminant Analysis, and (3) Local Binary Patterns. In the second type, we apply two super-resolution methods: (1) a model based method and (2) a feature based method on the low-resolution (both real and downsampled) images and then compute the face recognition accuracy.

    KW - EWI-23950

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    KW - IR-87825

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    KW - METIS-300149

    KW - Performance Evaluation

    M3 - Conference contribution

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    SN - 978-90-365-0000-5

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    Peng Y, Spreeuwers L, Gökberk B, Veldhuis R. Comparison of super-resolution benefits for downsampled iages and real low-resolution data. In 34th WIC Symposium on Information Theory in the Benelux and the 3rd Joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux 2013: Leuven, Belgium, 30-31 May 2013. Werkgemeenschap voor Informatie- en Communicatietheorie (WIC). 2013. p. 244-251