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Proceedings Paper

Multi-feature fusion based approach for robust face recognition
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Paper Abstract

How to describe an image accurately with the most useful information is the key issue of any face recognition task. Therefore, finding efficient and discriminative facial information that should be stable under different conditions of the image acquisition process is a huge challenge. Most existing approaches use only one type of features. In this paper, we argue that a robust face recognition technique requires several different kinds of information to be taken into account, suggesting the incorporation of several feature sets into a single fused one. Therefore, a new technique that combines the facial shape with the local structure and texture of the face image is proposed, namely multi-feature fusion (MFF). It is based on local boosted features (LBF) and Gabor wavelets techniques. Given an input image, the LBF histogram and Gabor features histogram are built separately. Then a final MFF feature descriptor is formed by concatenating these three histograms, which feeds to the support vector machine (SVM) classifier to recognize the face image. The proposed MFF approach is evaluated on three different face datasets and provided promising results.

Paper Details

Date Published: 14 May 2018
PDF: 6 pages
Proc. SPIE 10668, Mobile Multimedia/Image Processing, Security, and Applications 2018, 1066808 (14 May 2018); doi: 10.1117/12.2305371
Show Author Affiliations
Almabrok Essa, Univ. of Dayton (United States)
Vijayan Asari, Univ. of Dayton (United States)

Published in SPIE Proceedings Vol. 10668:
Mobile Multimedia/Image Processing, Security, and Applications 2018
Sos S. Agaian; Sabah A. Jassim, Editor(s)

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