Abstract Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are
two popular feature extraction techniques in statistical pattern recognition field. Due to small …

Facial feature extraction plays an important step in automated visual interpretation and
human face recognition. Detecting facial feature is a crucial role in a wide variety of …

In this paper, we propose a novel kernel named Adaptive Data-dependent Matrix Norm
Based Gaussian Kernel (ADM-Gaussian kernel) for facial feature extraction. As a popular …

Automatic facial feature extraction is one of the most important and attempted problems in
computer vision. It is a necessary step in face recognition, facial image compression. There …

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary
discriminant criterion for pattern classification that utilizes the generalized scatter difference …

Appearance-based methods, especially linear discriminant analysis (LDA), have been very
successful in facial feature extraction, but the recognition performance of LDA is often …

Linear discriminant analysis (LDA) is a typical feature extraction method, but there exist at
least two critical drawbacks in LDA: the small sample size problem and the rank limitation …

Linear Discriminant Analysis (LDA) is an effective feature extraction method, but there exist
at least two critical drawbacks in it: small sample size problem and rank limitation. In order to …

A new LDA-based face recognition system is presented in this paper. Linear discriminant
analysis (LDA) is one of the most popular linear projection techniques for feature extraction …

In this paper, we try to extend Fisher linear discriminant analysis (FLD) to the singular cases.
Firstly, PCA is used to reduce the dimension of feature space to N-1 (N denotes the number …