کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
529062 | 869627 | 2015 | 8 صفحه PDF | دانلود رایگان |
• A highly discriminative sub-space learning method is proposed.
• A novel collaborative between-class reconstruction error is maximized.
• The small class-specific between-class reconstruction error is emphasized.
• Linear regression classification performs well in the learned sub-space.
This paper proposes a novel face recognition method that improves Huang’s linear discriminant regression classification (LDRC) algorithm. The original work finds a discriminant subspace by maximizing the between-class reconstruction error and minimizing the within-class reconstruction error simultaneously, where the reconstruction error is obtained using Linear Regression Classification (LRC). However, the maximization of the overall between-class reconstruction error is easily dominated by some large class-specific between-class reconstruction errors, which makes the following LRC erroneous. This paper adopts a better between-class reconstruction error measurement which is obtained using the collaborative representation instead of class-specific representation and can be regarded as the lower bound of all the class-specific between-class reconstruction errors. Therefore, the maximization of the collaborative between-class reconstruction error maximizes each class-specific between-class reconstruction and emphasizes the small class-specific between-class reconstruction errors, which is beneficial for the following LRC. Extensive experiments are conducted and the effectiveness of the proposed method is verified.
Journal: Journal of Visual Communication and Image Representation - Volume 31, August 2015, Pages 312–319