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 …

The explainable human–computer interaction (HCI) is about designing approaches capable
of using cognitive characteristics like humans. One such characteristic is human vision and …

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 …

Face recognition (FR) with a single training sample per person (STSPP) is a very
challenging problem due to the lack of information to predict the variations in the query …

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 …

This paper presents two new techniques, viz., DWT Dual-subband Frequency-domain
Feature Extraction (DDFFE) and Threshold-Based Binary Particle Swarm Optimization …

Face recognition is receiving a significant attention due to the need of facing important
challenges when develo** real applications under unconstrained environments. The …

With the aid of a universal facial variation dictionary, sparse representation based classifier
(SRC) has been naturally extended for face recognition (FR) with single sample per person …