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 this paper, through extension of the present methods and based on error minimization,
two fast and efficient layer-by-layer pre-training methods are proposed for initializing deep …

Nonlinear components extracted from deep structures of bottleneck neural networks exhibit
a great ability to express input space in a low-dimensional manifold. Sharing and combining …

The performance and robustness of face recognition are largely determined by the data
samples used for model training. To obtain more representative samples of a face, this …

In this study, a new method for single sample face recognition problem based on local dual‐
tree complex wavelet transform (DT‐CWT) representation is proposed. The proposed …

Lack of training samples always affects the performance and robustness of face recognition.
Generating virtual samples is one of effective methods to expand the training set. When the …

Preliminary experiments on the deep architectures of the Auto-Associative Neural Networks
demonstrated that they have a fascinating ability in complex nonlinear feature extraction …

The issue of input variability resulting from writer changes is one of the most crucial factors
influencing the effectiveness of handwritten character recognition systems. A solution to this …

In this paper, an approach is proposed in order to solve the problem of pose variations for
face recognition using single image per person by means of neural network models …

Average Hausdorff distance that is an efficient measurement is widely used in face
recognition method for measuring the dissimilarity between two sets of features. The New …