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Pattern Analysis and Applications

Erschienen in:

21.09.2016 | Theoretical Advances

Recursive reconstruction of non-facial components using support vector data description

verfasst von: Sang-Woong Lee, Heon Jeong, Jeong-Seon Park

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2018

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Abstract

To distinguish non-facial components, such as glasses, scarves, and hair, this study proposes a recursive reconstruction method using support vector data description that estimates the approximate face by determining the minimum distance between the transformed input and the trained hyperball in a feature space. The performance of this reconstruction is further improved by replacing the non-facial components with an average face for the initial reconstruction and recursively updating the face using support vector data description. To validate the effectiveness of the proposed method, we perform reconstruction experiments by varying the position and size of non-facial components using images from the AR, the Korean, and the XM2VTS facial database, and measure the reconstruction errors. The experimental results with three databases and some live face images confirm the effectiveness of the proposed method for reconstructing non-facial components.

Vorheriger Artikel An algorithm on sign words extraction and recognition of continuous Persian sign language based on motion and shape features of hands

Nächster Artikel Dedicated feature descriptor for outdoor augmented reality detection

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Titel: Recursive reconstruction of non-facial components using support vector data description
verfasst von: Sang-Woong Lee
Heon Jeong
Jeong-Seon Park
Publikationsdatum: 21.09.2016
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 2/2018
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-016-0580-9

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