One of the main challenges faced by the current face recognition techniques lies in the
difficulties of collecting samples. Fewer samples per person mean less laborious effort for …

Conventional appearance-based face recognition methods usually assume that there are
multiple samples per person (MSPP) available for discriminative feature extraction during …

Where possible, automation has been a common response of humankind to many activities
that have to be repeated numerous times. The routine identification of specimens of …

This paper targets learning robust image representation for single training sample per
person face recognition. Motivated by the success of deep learning in image representation …

Single-Sample Face Recognition (SSFR) is a computer vision challenge. In this scenario,
there is only one example from each individual on which to train the system, making it …

In face recognition, the Fisherface approach based on Fisher linear discriminant analysis
(FLDA) has obtained some success. However, FLDA fails when each person just has one …

At present, there are many methods for frontal view face recognition. However, few of them
can work well when only one example image per class is available. In this paper, we present …

Studying the inherently high-dimensional nature of the data in a lower dimensional manifold
has become common in recent years. This is generally known as dimensionality reduction. A …

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 …

Real-world face recognition systems often have to face the single sample per person (SSPP)
problem, that is, only a single training sample for each person is enrolled in the database. In …