Relation Learning- A New Approach to Face Recognition

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    Abstract

    Most of current machine learning methods used in face recognition systems require sufficient data to build a face model or face data description. However insufficient data is currently a common issue. This paper presents a new learning approach to tackle this issue. The proposed learning method employs not only the data in facial images but also relations between them to build relational face models. Preliminary experiments performed on the AT&T and FERET face corpus show a significant improvement for face recognition rate when only a small facial data set is available for training.
    Original languageEnglish
    Title of host publicationInternational Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011)
    Subtitle of host publicationLecture Notes in Computer Science
    Place of PublicationBelgium
    PublisherSpringer
    Pages566-575
    Number of pages10
    Volume6915
    ISBN (Electronic)9783642236877
    ISBN (Print)9783642236860
    DOIs
    Publication statusPublished - 2011
    Event13th International Conference, ACIVS 2011: Advances Concepts for Intelligent Vision System - Ghent, Ghent, Belgium
    Duration: 22 Aug 201125 Nov 2011

    Conference

    Conference13th International Conference, ACIVS 2011
    CountryBelgium
    CityGhent
    Period22/08/1125/11/11

    Fingerprint

    Face recognition
    Data description
    Learning systems
    Experiments

    Cite this

    Tran, D., Huang, X., & Chetty, G. (2011). Relation Learning- A New Approach to Face Recognition. In International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011): Lecture Notes in Computer Science (Vol. 6915, pp. 566-575). Belgium: Springer. https://doi.org/10.1007/978-3-642-23687-7_51
    Tran, Dat ; Huang, Xu ; Chetty, Girija. / Relation Learning- A New Approach to Face Recognition. International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011): Lecture Notes in Computer Science. Vol. 6915 Belgium : Springer, 2011. pp. 566-575
    @inproceedings{27ed34123e7940c0afaac74d7b64605a,
    title = "Relation Learning- A New Approach to Face Recognition",
    abstract = "Most of current machine learning methods used in face recognition systems require sufficient data to build a face model or face data description. However insufficient data is currently a common issue. This paper presents a new learning approach to tackle this issue. The proposed learning method employs not only the data in facial images but also relations between them to build relational face models. Preliminary experiments performed on the AT&T and FERET face corpus show a significant improvement for face recognition rate when only a small facial data set is available for training.",
    keywords = "Face Identification, Similarity Score, Relation Learning, Support Vectore Machine",
    author = "Dat Tran and Xu Huang and Girija Chetty",
    year = "2011",
    doi = "10.1007/978-3-642-23687-7_51",
    language = "English",
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    pages = "566--575",
    booktitle = "International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011)",
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    Tran, D, Huang, X & Chetty, G 2011, Relation Learning- A New Approach to Face Recognition. in International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011): Lecture Notes in Computer Science. vol. 6915, Springer, Belgium, pp. 566-575, 13th International Conference, ACIVS 2011, Ghent, Belgium, 22/08/11. https://doi.org/10.1007/978-3-642-23687-7_51

    Relation Learning- A New Approach to Face Recognition. / Tran, Dat; Huang, Xu; Chetty, Girija.

    International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011): Lecture Notes in Computer Science. Vol. 6915 Belgium : Springer, 2011. p. 566-575.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AB - Most of current machine learning methods used in face recognition systems require sufficient data to build a face model or face data description. However insufficient data is currently a common issue. This paper presents a new learning approach to tackle this issue. The proposed learning method employs not only the data in facial images but also relations between them to build relational face models. Preliminary experiments performed on the AT&T and FERET face corpus show a significant improvement for face recognition rate when only a small facial data set is available for training.

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    KW - Similarity Score

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    BT - International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011)

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    Tran D, Huang X, Chetty G. Relation Learning- A New Approach to Face Recognition. In International Conference on Advanced Concepts for Intelligent Vision System (ACIVS 2011): Lecture Notes in Computer Science. Vol. 6915. Belgium: Springer. 2011. p. 566-575 https://doi.org/10.1007/978-3-642-23687-7_51