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Face Recognition under Complex Illumination Based on Multi-scale Weberface and Local Binary Pattern |
Shuhong Cheng, Jie Liu, Hong Wei, Xiaoman Qu |
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Abstract A novel method based on Multi-scale Weberface (MWF) and Local Binary
Pattern (LBP) is proposed to reduce illumination effects for face
recognition in this paper. The method starts with pre-processing
face image by utilizing contrast stretching transformation that
reduces the effect of different lighting to some extent. Then
different scale Weberfaces are extracted to calculate MWF based on
weighted method as multi-scale illumination insensitive face.
Finally the study abstracts LBP feature from MWF as face recognition
feature under complex illumination and employs multi-class Support
Vector Machine (SVM) for face authentication. The experiments are
executed both on CMU-PIE and Yale B face databases. The experimental
results have indicated that the proposed method can effectively
eliminate the influence of face recognition under complex
illumination and the recognition rates are superior to Weberface
(WF), even single sample images with serious light as training
sample images can also work well. Furthermore, this study also has
shown that our method is more robust to noise and more suitable to
the actual system for face authentication.
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DOI:10.12733/jics20106292 |
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