NDLI: Face Recognition Based on Local Binary Patterns with Threshold

Content Provider	IEEE Xplore Digital Library
Author	Jun Meng Yumao Gao Xiukun Wang Tsauyoung Lin Jianying Zhang
Copyright Year	2010
Abstract	This paper presents a novel and efficient face recognition technique based on Local Binary Pattern (LBP) with threshold for resolving traditional LBP’s weakness of extracting global features. By setting a threshold to enhance the robustness to noise such as light and extract the global features of face preferably. Combining the local features by LBP with global features as the total features of the image is more power discriminating. Principal Component Analysis (PCA) and linear discriminate analysis (LDA) are used to reduce the dimensionality and optimize discriminative recognition respectively. The proposed method is tested and evaluated not only on ORL datasets but also on YALE datasets and yields a recognition rate of 98%, the experimental results show that the method is valid and feasible.
Starting Page	352
Ending Page	356
File Size	1030034
Page Count	5
File Format	PDF
ISBN	9781424479641
DOI	10.1109/GrC.2010.72
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-08-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Accuracy Face recognition local binary pattern face recognition Feature extraction Robustness threshold Face Pixel linear discriminate analysis
Content Type	Text
Resource Type	Article

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Face Recognition Based on Local Binary Patterns with Threshold