It has been known for many years that identifying familiar faces is much easier than
identifying unfamiliar faces, and that this familiar face advantage persists across a range of …

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach.
The algorithms of LDA usually perform well under the following two assumptions. The first …

Image classification has become one of the main tasks in the field of computer vision
technologies. In this context, a recent algorithm called CapsNet that implements an …

This book provides the state-of-art account of authentication technologies which covers not
only basic authentication methodologies but also emerging technologies which are yet to be …

Face recognition systems due to their significant application in the security scopes, have
been of great importance in recent years. The existence of an exact balance between the …

In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-
2DLDA) with robust performance. Different from the conventional two-dimensional linear …

Recent advances have shown that algorithms with (2D) matrix-based representation perform
better than the traditional (1D) vector-based ones. In particular, 2D-LDA has been widely …

Conventionally, autoencoders are unsupervised representation learning tools. In this work,
we propose a novel discriminative autoencoder. Use of supervised discriminative learning …

We propose a novel human face recognition approach in this paper, based on two-
dimensional Gabor filtering and supervised classification. The feature extraction technique …

The classical linear discriminant analysis (LDA) algorithm has three primary drawbacks, ie,
small sample size problem, sensitivity to noise and outliers, and inability to deal with multi …