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In this paper, a novel discriminant feature extraction algorithm employing center-based distance is proposed for face recognition. This new method, which is a supervised linear dimensionality reduction and feature extraction approach, computes the center-based distance between each training sample-pairs in the same class and the distance between each training sample-pair belonging to different classes...
Locally discriminating projection (LDP) is a new subspace feature extraction method which takes special consideration of both the local information and the class information. As the LDP model is linear, it may fail to extract the nonlinear features. This paper proposes to address this problem using an alternative formulation, kernel locally preserving projection (KLDP). The proposed method consists...
Face recognition is a very active field for research in the field of pattern recognition. To improve the performance of feature extraction in face recognition, a novel feature extraction method named as minimal linear discriminant analysis based on independent component analysis (ICA) is proposed. Therefore, the singular problem of the within-class scatter matrix will be avoided, and linear discriminant...
In this paper, we propose a two-dimensional Inverse Fisher Discriminant Analysis (2DIFDA) method for feature extraction and face recognition. This method combines the ideas of two-dimensional principal component analysis and Inverse FDA and it can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the inverse fisher discriminant criterion. Experiments...
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