This paper proposes a mini-batch discriminative feature weighting methodology for
minimization of classification error in datasets with considerable number of classes and poor …

This paper presents a novel approach for face recognition by boosting statistical local
features based classifiers. The face image is scanned with a scalable sub-window from …

Local Binary Pattern (LBP) is a powerful means of texture description that has achieved
great success in face analysis area. In this paper, we propose a face recognition approach …

In this paper, we propose a novel representation, called Multi-scale Block Local Binary
Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been …

The accurate description of face images under variable illumination, pose and face
expression conditions is a topic that has attracted the attention of researchers in recent …

This paper proposes a novel face recognition methodology, which is based on the
combination of the texture operator, namely Multi-scale Local Binary Pattern (MSLBP), face …

Due to its invariance to monotonic grayscale transformation and simple computation, Local
Binary Pattern (LBP) is broadly used as feature extractor in face recognition tasks in recent …

The exiting local binary pattern (LBP) operators have two disadvantages:(1) They produce
rather long histograms, which slow down the recognition speed especially on large-scale …

For single sample face recognition, the approaches based on statistical learning are always
suffering from the generalizability problem because of small samples. This paper proposes a …

In this paper, we propose a feature extraction method based on decision level fusion of local
features for simple yet robust face recognition. The origin face is first divided into smaller …