Combining Illumination Normalization Methods for Better Face Recognition

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

    1 Citation (Scopus)

    Abstract

    Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.
    Original languageEnglish
    Title of host publicationAdvances in biometrics
    Subtitle of host publicationThird International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings
    EditorsMassimo Tistarelli, Mark S. Nixon
    Place of PublicationBerlin
    PublisherSpringer
    Pages404-413
    Number of pages10
    ISBN (Print)978-3-642-01792-6
    DOIs
    Publication statusPublished - 4 Jun 2009
    Event3rd IAPR International Conference on Biometrics, ICB 2009 - University of Sassari, Alghero, Italy
    Duration: 2 Jun 20095 Jun 2009
    Conference number: 3

    Publication series

    NameLecture Notes in Computer Science
    PublisherSpringer Verlag
    Volume5558
    ISSN (Print)0302-9743

    Conference

    Conference3rd IAPR International Conference on Biometrics, ICB 2009
    Abbreviated titleICB
    CountryItaly
    CityAlghero
    Period2/06/095/06/09

    Fingerprint

    Face recognition
    Lighting
    Fusion reactions
    Biometrics
    Pixels

    Keywords

    • METIS-263914
    • Face Recognition
    • Illumination Normalization
    • Decision level fusion
    • Model-based Face Illumination Correction
    • EWI-15683
    • hybrid fusion
    • Score-level fusion
    • Local Binary Patterns
    • SCS-Safety
    • IR-67800

    Cite this

    Boom, B. J., Tao, Q., Spreeuwers, L. J., & Veldhuis, R. N. J. (2009). Combining Illumination Normalization Methods for Better Face Recognition. In M. Tistarelli, & M. S. Nixon (Eds.), Advances in biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings (pp. 404-413). (Lecture Notes in Computer Science; Vol. 5558). Berlin: Springer. https://doi.org/10.1007/978-3-642-01793-3_42
    Boom, B.J. ; Tao, Q. ; Spreeuwers, Lieuwe Jan ; Veldhuis, Raymond N.J. / Combining Illumination Normalization Methods for Better Face Recognition. Advances in biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. editor / Massimo Tistarelli ; Mark S. Nixon. Berlin : Springer, 2009. pp. 404-413 (Lecture Notes in Computer Science).
    @inproceedings{f2eaf7a093cc462f9ae2d777a5d599f9,
    title = "Combining Illumination Normalization Methods for Better Face Recognition",
    abstract = "Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.",
    keywords = "METIS-263914, Face Recognition, Illumination Normalization, Decision level fusion, Model-based Face Illumination Correction, EWI-15683, hybrid fusion, Score-level fusion, Local Binary Patterns, SCS-Safety, IR-67800",
    author = "B.J. Boom and Q. Tao and Spreeuwers, {Lieuwe Jan} and Veldhuis, {Raymond N.J.}",
    note = "10.1007/978-3-642-01793-3_42",
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    Boom, BJ, Tao, Q, Spreeuwers, LJ & Veldhuis, RNJ 2009, Combining Illumination Normalization Methods for Better Face Recognition. in M Tistarelli & MS Nixon (eds), Advances in biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. Lecture Notes in Computer Science, vol. 5558, Springer, Berlin, pp. 404-413, 3rd IAPR International Conference on Biometrics, ICB 2009, Alghero, Italy, 2/06/09. https://doi.org/10.1007/978-3-642-01793-3_42

    Combining Illumination Normalization Methods for Better Face Recognition. / Boom, B.J.; Tao, Q.; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.

    Advances in biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. ed. / Massimo Tistarelli; Mark S. Nixon. Berlin : Springer, 2009. p. 404-413 (Lecture Notes in Computer Science; Vol. 5558).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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    N2 - Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.

    AB - Face Recognition under uncontrolled illumination conditions is partly an unsolved problem. There are two categories of illumination normalization methods. The first category performs a local preprocessing, where they correct a pixel value based on a local neighborhood in the images. The second category performs a global preprocessing step, where the illumination conditions and the face shape of the entire image are estimated. We use two illumination normalization methods from both categories, namely Local Binary Patterns and Model-based Face Illumination Correction. The preprocessed face images of both methods are individually classified with a face recognition algorithm which gives us two similarity scores for a face image. We combine the similarity scores using score-level fusion, decision-level fusion and hybrid fusion. In our previous work, we show that combining the similarity score of different methods using fusion can improve the performance of biometric systems. We achieved a significant performance improvement in comparison with the individual methods.

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    KW - Score-level fusion

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    KW - SCS-Safety

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    A2 - Nixon, Mark S.

    PB - Springer

    CY - Berlin

    ER -

    Boom BJ, Tao Q, Spreeuwers LJ, Veldhuis RNJ. Combining Illumination Normalization Methods for Better Face Recognition. In Tistarelli M, Nixon MS, editors, Advances in biometrics: Third International Conference, ICB 2009, Alghero, Italy, June 2-5, 2009. Proceedings. Berlin: Springer. 2009. p. 404-413. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-642-01793-3_42