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LBP-Ferns-Based Feature Extraction for Robust Facial Recognition

Authors
Jung, June-YoungKim, Seung-WookYoo, Cheol-HwanPark, Won-JaeKo, Sung-Jea
Issue Date
11월-2016
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Facial recognition; feature extraction; local binary patterns; random-ferns; orthogonal linear discriminant analysis
Citation
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS, v.62, no.4, pp.446 - 453
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON CONSUMER ELECTRONICS
Volume
62
Number
4
Start Page
446
End Page
453
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/87126
ISSN
0098-3063
Abstract
Most facial recognition (FR) systems first extract discriminative features from a facial image and then perform classification. This paper proposes a method aimed at representing human facial traits and a low-dimensional feature extraction method using orthogonal linear discriminant analysis (OLDA). The proposed feature relies on a local binary pattern to represent texture information and random ferns to build a structural model. By concatenating its feature vectors, the proposed method achieves a high-dimensional descriptor of the input facial image. In general, the feature dimension is highly related to its discriminative ability. However, higher dimensionality is more complex to compute. Thus, dimensionality reduction is essential for practical FR applications. OLDA is employed to reduce the dimension of the extracted features and improve discriminative performance. With a representative FR database, the proposed method demonstrates a higher recognition rate and low computational complexity compared to existing FR methods. In addition, with a facial image database with disguises, the proposed algorithm demonstrates outstanding performance(1).
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