The Single Sample per Person Problem is a challenging problem for face recognition
algorithms. Patch-based methods have obtained some promising results for this problem. In …

Among various signals that can be obtained from humans, facial image is one of the hottest
topics in the field of pattern recognition and machine learning due to its diverse variations. In …

We introduce a novel method for face recognition from image sets. In our setting each test
and training example is a set of images of an individual's face, not just a single image, so …

We consider the problem of feature-based face recognition in the setting where only a single
example of each face is available for training. The mixture-distance technique we introduce …

Face identification under small sample conditions is currently an active research area. In a
case of very few reference samples, optimally exploiting the training data to make a model …

In this paper a novel technique for face recognition is proposed. Using the statistical local
feature analysis (LFA) technique, a set of feature points is extracted from each face image, at …

In this paper, we propose the effective similarity feature-based selection and classification
algorithm to select similarity features on the training images and to classify face images in …

In this paper we have presented a novel approach for face recognition. The proposed
method is based on the facial landmarks such as eyes, nose, lips etc. In this approach, first …

Face recognition has been a long standing problem in computer vision. Histograms of
Oriented Gradients (HOGs) and Local Binary Patterns (LBPs) have proven to be an effective …

LBP is an effective descriptor for face recognition. LBP encodes the ordinal relationship
between the neighborhood samplings and the central one to obtain robust face …