The Scale Invariant Feature Transform (SIFT) proposed by David G. Lowe has been used in face recognition and proved to perform well. Recently, a new detector and descriptor, named Speed-Up Robust Features (SURF) suggested by Herbert Bay, attracts people’s attentions. SURF is a scale and in-plane rotation invariant detector and descriptor with comparable or even better performance with SIFT. Bec...

Face recognition is promising as an active research area spanning several disciplines such as computer vision, patternrecognition, neural network and image processing. It plays animportant role in many application areas such as authentication, human machine and surveillance. Human beings appear to recognize faces in cluttered scenes with relative ease having the ability to identify coarsely qua...

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a k...

Many classic and contemporary face recognition algorithms work well on public data sets, but degrade sharply when they are used in a real recognition system. This is mostly due to the difficulty of simultaneously handling variations in illumination, image misalignment, and occlusion in the test image. We consider a scenario where the training images are well controlled and test images are only ...

The wide range of variations in human face due to view point, pose, illumination and expression deteriorate the recognition performance of the existing Face recognition systems. This paper proposes a new approach to face recognition problem using Legendre moments for representing features and nearest neighbor classifier for classification. The Legendre moments are orthogonal and scale invariant...

In this paper, we present an ordinal feature based method for face recognition. Ordinal features are used to represent faces. Hamming distance of many local sub-windows is computed to evaluate differences of two ordinal faces. AdaBoost learning is finally applied to select most effective hamming distance based weak classifiers and build a powerful classifier. Experiments demonstrate good result...