A SSIM-Based Approach for Finding Similar Facial Expressions

Abhinav Dhall, Akshay Asthana, Roland Goecke

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

    6 Citations (Scopus)

    Abstract

    There are various scenarios where finding the most similar expression is the requirement rather than classifying one into discrete, pre-defined classes, for example, for facial expression transfer and facial expression based automatic album generation. This paper proposes a novel method for finding the most similar facial expression. Instead of the regular L2 norm distance, we investigate the use of the Structural SIMilarity (SSIM) metric for similarity comparison as a distance metric in a nearest neighbour unsupervised algorithm. The feature vectors are generated using Active Appearance Models (AAM). We also demonstrate how this technique can be extended and used for finding corresponding facial expression images across two or more subjects, which is useful in applications such as facial animation and automatic expression transfer. Person-independent facial expression performance results are shown on the Multi-PIE, FEEDTUM and AVOZES databases. We also compare the performance of the SSIM metric versus other distance metrics in a nearest neighbour search for finding the most similar facial expression to a given image
    Original languageEnglish
    Title of host publication2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop
    EditorsKevin Bowyer, Marian Bartlett, Rainer Stiefelhagen
    Place of PublicationUSA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages815-820
    Number of pages6
    ISBN (Electronic)9781424491414
    ISBN (Print)9781424491407
    DOIs
    Publication statusPublished - 2011
    Event 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011) - Santa Barbara, Santa Barbara, United States
    Duration: 21 Mar 201125 Mar 2011

    Conference

    Conference 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011)
    CountryUnited States
    CitySanta Barbara
    Period21/03/1125/03/11

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    Cite this

    Dhall, A., Asthana, A., & Goecke, R. (2011). A SSIM-Based Approach for Finding Similar Facial Expressions. In K. Bowyer, M. Bartlett, & R. Stiefelhagen (Eds.), 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop (pp. 815-820). USA: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/FG.2011.5771354
    Dhall, Abhinav ; Asthana, Akshay ; Goecke, Roland. / A SSIM-Based Approach for Finding Similar Facial Expressions. 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. editor / Kevin Bowyer ; Marian Bartlett ; Rainer Stiefelhagen. USA : IEEE, Institute of Electrical and Electronics Engineers, 2011. pp. 815-820
    @inproceedings{bf98aa5299694c8c846f5e426555db99,
    title = "A SSIM-Based Approach for Finding Similar Facial Expressions",
    abstract = "There are various scenarios where finding the most similar expression is the requirement rather than classifying one into discrete, pre-defined classes, for example, for facial expression transfer and facial expression based automatic album generation. This paper proposes a novel method for finding the most similar facial expression. Instead of the regular L2 norm distance, we investigate the use of the Structural SIMilarity (SSIM) metric for similarity comparison as a distance metric in a nearest neighbour unsupervised algorithm. The feature vectors are generated using Active Appearance Models (AAM). We also demonstrate how this technique can be extended and used for finding corresponding facial expression images across two or more subjects, which is useful in applications such as facial animation and automatic expression transfer. Person-independent facial expression performance results are shown on the Multi-PIE, FEEDTUM and AVOZES databases. We also compare the performance of the SSIM metric versus other distance metrics in a nearest neighbour search for finding the most similar facial expression to a given image",
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    Dhall, A, Asthana, A & Goecke, R 2011, A SSIM-Based Approach for Finding Similar Facial Expressions. in K Bowyer, M Bartlett & R Stiefelhagen (eds), 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 815-820, 2011 IEEE International Conference on Automatic Face & Gesture Recognition and Workshops (FG 2011), Santa Barbara, United States, 21/03/11. https://doi.org/10.1109/FG.2011.5771354

    A SSIM-Based Approach for Finding Similar Facial Expressions. / Dhall, Abhinav; Asthana, Akshay; Goecke, Roland.

    2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. ed. / Kevin Bowyer; Marian Bartlett; Rainer Stiefelhagen. USA : IEEE, Institute of Electrical and Electronics Engineers, 2011. p. 815-820.

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

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    AU - Asthana, Akshay

    AU - Goecke, Roland

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    N2 - There are various scenarios where finding the most similar expression is the requirement rather than classifying one into discrete, pre-defined classes, for example, for facial expression transfer and facial expression based automatic album generation. This paper proposes a novel method for finding the most similar facial expression. Instead of the regular L2 norm distance, we investigate the use of the Structural SIMilarity (SSIM) metric for similarity comparison as a distance metric in a nearest neighbour unsupervised algorithm. The feature vectors are generated using Active Appearance Models (AAM). We also demonstrate how this technique can be extended and used for finding corresponding facial expression images across two or more subjects, which is useful in applications such as facial animation and automatic expression transfer. Person-independent facial expression performance results are shown on the Multi-PIE, FEEDTUM and AVOZES databases. We also compare the performance of the SSIM metric versus other distance metrics in a nearest neighbour search for finding the most similar facial expression to a given image

    AB - There are various scenarios where finding the most similar expression is the requirement rather than classifying one into discrete, pre-defined classes, for example, for facial expression transfer and facial expression based automatic album generation. This paper proposes a novel method for finding the most similar facial expression. Instead of the regular L2 norm distance, we investigate the use of the Structural SIMilarity (SSIM) metric for similarity comparison as a distance metric in a nearest neighbour unsupervised algorithm. The feature vectors are generated using Active Appearance Models (AAM). We also demonstrate how this technique can be extended and used for finding corresponding facial expression images across two or more subjects, which is useful in applications such as facial animation and automatic expression transfer. Person-independent facial expression performance results are shown on the Multi-PIE, FEEDTUM and AVOZES databases. We also compare the performance of the SSIM metric versus other distance metrics in a nearest neighbour search for finding the most similar facial expression to a given image

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    Dhall A, Asthana A, Goecke R. A SSIM-Based Approach for Finding Similar Facial Expressions. In Bowyer K, Bartlett M, Stiefelhagen R, editors, 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshop. USA: IEEE, Institute of Electrical and Electronics Engineers. 2011. p. 815-820 https://doi.org/10.1109/FG.2011.5771354