A comparative study of baseline algorithms of face recognition

Zahid Mehmood, Tauseef Ali, Shahid Khattak, Samee U. Khan

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

    8 Citations (Scopus)
    146 Downloads (Pure)

    Abstract

    In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.
    Original languageUndefined
    Title of host publication12th International Conference on Frontiers of Information Technology, FIT 2014
    Place of PublicationUSA
    PublisherIEEE
    Pages263-268
    Number of pages6
    ISBN (Print)978-1-4799-7505-1
    DOIs
    Publication statusPublished - Dec 2014

    Publication series

    Name
    PublisherIEEE

    Keywords

    • SCS-Safety
    • METIS-312728
    • IR-97324
    • EWI-26323

    Cite this

    Mehmood, Z., Ali, T., Khattak, S., & Khan, S. U. (2014). A comparative study of baseline algorithms of face recognition. In 12th International Conference on Frontiers of Information Technology, FIT 2014 (pp. 263-268). USA: IEEE. https://doi.org/10.1109/FIT.2014.56
    Mehmood, Zahid ; Ali, Tauseef ; Khattak, Shahid ; Khan, Samee U. / A comparative study of baseline algorithms of face recognition. 12th International Conference on Frontiers of Information Technology, FIT 2014. USA : IEEE, 2014. pp. 263-268
    @inproceedings{9fa74c149dac4cefbd32577aadef4e5a,
    title = "A comparative study of baseline algorithms of face recognition",
    abstract = "In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.",
    keywords = "SCS-Safety, METIS-312728, IR-97324, EWI-26323",
    author = "Zahid Mehmood and Tauseef Ali and Shahid Khattak and Khan, {Samee U.}",
    note = "10.1109/FIT.2014.56",
    year = "2014",
    month = "12",
    doi = "10.1109/FIT.2014.56",
    language = "Undefined",
    isbn = "978-1-4799-7505-1",
    publisher = "IEEE",
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    booktitle = "12th International Conference on Frontiers of Information Technology, FIT 2014",
    address = "United States",

    }

    Mehmood, Z, Ali, T, Khattak, S & Khan, SU 2014, A comparative study of baseline algorithms of face recognition. in 12th International Conference on Frontiers of Information Technology, FIT 2014. IEEE, USA, pp. 263-268. https://doi.org/10.1109/FIT.2014.56

    A comparative study of baseline algorithms of face recognition. / Mehmood, Zahid; Ali, Tauseef; Khattak, Shahid; Khan, Samee U.

    12th International Conference on Frontiers of Information Technology, FIT 2014. USA : IEEE, 2014. p. 263-268.

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

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    AU - Khattak, Shahid

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    N2 - In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.

    AB - In this paper we present a comparative study of two well-known face recognition algorithms. The contribution of this work is to reveal the robustness of each FR algorithm with respect to various factors, such as variation in pose and low resolution of the images used for recognition. This evaluation is useful for practical applications where the types of the expected images are known. The two FR algorithms studied in this work are Principal Component Analysis (PCA) and AdaBoost with Linear Discriminant Analysis (LDA) as a weak learner. Images from multi-pie database are used for evaluation. Simulation results revealed that given one gallery (Training) face image and four different pose images as a probe (Testing), PCA based system is more accurate in recognizing pose, while AdaBoost was more robust on recognizing low resolution images.

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    Mehmood Z, Ali T, Khattak S, Khan SU. A comparative study of baseline algorithms of face recognition. In 12th International Conference on Frontiers of Information Technology, FIT 2014. USA: IEEE. 2014. p. 263-268 https://doi.org/10.1109/FIT.2014.56