In many real-world applications such as face recognition and mobile robotics, we need to use an adaptive version of feature extraction techniques. In this paper, we introduce an a...
In this paper, a novel subspace method called diagonal principal component analysis (DiaPCA) is proposed for face recognition. In contrast to standard PCA, DiaPCA directly seeks t...
Face recognition is one of the most important image processing research topics which is widely used in personal identification, verification and security applications. In this pape...
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
This paper presents a 3D approach for recognizing faces based on Principal Component Analysis (PCA). The approach addresses the issue of proper 3D face alignment required by PCA f...