The number of the training samples per person has a significant impact on face recognition (FR) performance. For the single training sample per person (STSPP) problem, most traditional FR algorithms exhibit performance degradation owing to the limited information available to predict the variance of the query sample. This paper proposes a new method for the STSPP problem in FR, namely the Learn...

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. (PC)A, is proposed. (PC)A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on th...

Abstract Objectives Several studies have investigated the relationship between contemplative approaches and psychobiological stress response; however, this area of research is still new, mechanisms response attenuation training not been fully delineated, little known about effects practice on ways psychological experienced. This study aimed to explore first-person experience in meditation pract...

Keywords: Face recognition Fisher linear discriminant analysis (FLDA) Single training image per person Singular value decomposition (SVD) a b s t r a c t Fisher linear discriminant analysis (FLDA) has been widely used for feature extraction in face recognition. However, it cannot be used when each object has only one training sample because the intra-class variations cannot be statistically mea...