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

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

Face recognition is required in various applications, and major progress has been
witnessed in this area. Many face recognition algorithms have been proposed thus far; …

Color face recognition has more attention recently since it considered one of the most
popular biometric pattern recognitions. With a considerable development in multimedia …

Single sample per person (SSPP) recognition is one of the most challenging problems in
face recognition (FR) due to the lack of information to predict the variations in the query …

Single sample face recognition (SSFR) is a challenging research problem in which only one
face image per person is available for training. Moreover, the face image may have different …

In wake of COVID-19, the world has adapted to a new order. People have started wearing
mask on their faces to prevent getting infected. The present face recognition models are no …

Face recognition from Single Sample per Person (SSPP) is extremely challenging because
only one sample is available for each person. While many discriminant analysis methods …

A novel block-based bag of words (BBoW) method is proposed for robust face recognition. In
our approach, a face image is partitioned into multiple blocks, dense SIFT features are then …

In this work, we proposed a novel method, called Local Modified Zernike Moment per unit
Mass (LMZMPM), for face recognition, which is invariant to illumination, scaling, noise, in …