LBP-ferns-based feature extraction for robust facial recognition

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)....

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on consumer electronics 2016-11, Vol.62 (4), p.446-453
Main Authors: Jung, June-Young, Kim, Seung-Wook, Yoo, Cheol-Hwan, Park, Won-Jae, Ko, Sung-Jea
Format: Article
Language:English
Subjects:
Algorithms
Complexity
Dimensional analysis
Discriminant analysis
Face
Face recognition
Facial recognition
Feature extraction
Feature recognition
High definition video
Histograms
Human performance
Kernel
local binary patterns
orthogonal linear discriminant analysis
Pattern analysis
random-ferns
Robustness
Structural models
Texture recognition
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0098-3063
1558-4127
DOI:10.1109/TCE.2016.7838098